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facet_rank_by_basic_0.25_Mingzhi_relevance_top3.txt
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-------- Documents Indexed --------
Qid: 5.3.4 How much money did Goldman Sachs make in the last 20 years ?
Original Ranking nDCG@5 = 0.1858351
Original Recall@5 = 0.071428575
Processed query: Y:(money) AND Y:(Goldman Sachs) AND X:(the last 20 years) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 20 years
Average number of facet_X values in each document is 4.393617
Average number of documents tagged with each facet value in the facet_X field is : 3.6875
Facet: time: 0.8244681 nDCG@5 = 0.1858351 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.071428575 facet gains recall 0.0
Facet: year: 0.7659575 nDCG@5 = 0.1858351 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.071428575 facet gains recall 0.0
Facet: month: 0.17021276 nDCG@5 = 0.1858351 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.1858351, facet loss @5 = 0.1858351 novel docs @5 = 1 recall @5 = 0.071428575 facet gains recall 0.0
Facet: quarter: 0.13297872 nDCG@5 = 0.3652848 nDCG@5 nDCG gain = 0.1794497 **
facet gain @5 = 0.3652848, facet loss @5 = 0.1858351 novel docs @5 = 5 Facet: company: 0.10638298 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: united: 0.095744684 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: financial: 0.08510638 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: business: 0.07978723 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: services: 0.06382979 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: investment: 0.047872342 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: bank america: 0.047872342 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.09291755, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.33333334
facet_X, total recall @5 = 1.0 facet gains recall 0.9285714
................. Analyzing Facet field facet_Y..................
Field facet_Y: money goldman sachs
Average number of facet_Y values in each document is 2.1914895
Average number of documents tagged with each facet value in the facet_Y field is : 2.0295568
Facet: revenue: 0.2393617 nDCG@5 = 0.20205598 nDCG@5 nDCG gain = 0.016220883 **
facet gain @5 = 0.20205598, facet loss @5 = 0.1858351 novel docs @5 = 2 recall @5 = 0.14285715 facet gains recall 0.071428575
Facet: goldman sachs: 0.13297872 nDCG@5 = 0.1858351 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.071428575 facet gains recall 0.0
Facet: sales: 0.122340426 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.071428575
Facet: apple inc: 0.08510638 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: mobile phone: 0.047872342 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: automobile: 0.04255319 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: share finance: 0.03723404 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: profit accounting: 0.03723404 nDCG@5 = 0.264001 nDCG@5 nDCG gain = 0.07816592 **
facet gain @5 = 0.264001, facet loss @5 = 0.1858351 novel docs @5 = 3 Facet: price: 0.031914894 nDCG@5 = 0.078165926 nDCG@5 nDCG gain = -0.10766917
facet gain @5 = 0.078165926, facet loss @5 = 0.1858351 novel docs @5 = 1 Facet: toyota: 0.026595745 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.1858351
facet gain @5 = 0.0, facet loss @5 = 0.1858351 novel docs @5 = 0 Facet: united: 0.026595745 nDCG@5 = 0.123890065 nDCG@5 nDCG gain = -0.06194503
facet gain @5 = 0.123890065, facet loss @5 = 0.1858351 novel docs @5 = 1
facet_Y, discounted nDCG Gain @5 = -0.076696664, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.20205598, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.5, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.6666667
facet_Y, total recall @5 = 0.9285714 facet gains recall 0.8571428
For query 5.3.4, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 5.3.4, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.20205598, alpha = 0 (only consider documents covered by facets).
For query 5.3.4, on either X or Y field, the maximum binary nDCG Gain @5 = 0.5, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.7.1 How does the revenue of Goldman Sachs compare over the past 15 years ?
Qid: 5.7.2 What was the revenue of Goldman Sachs in 1990 compared to future revenues ?
Qid: 5.7.3 What was the revenue of Goldman Sachs in 2005 compared to previous years ?
Original Ranking nDCG@5 = 0.2371977
Original Recall@5 = 0.5
Processed query: Y:(the revenue) AND Y:(Goldman Sachs) AND X:(to previous years) AND X:(in 2005) AND IMCategory:(Relative-Difference) AND focusX:(to previous years,in 2005)
................. Analyzing Facet field facet_X..................
Field facet_X: years 2005
Average number of facet_X values in each document is 4.1614585
Average number of documents tagged with each facet value in the facet_X field is : 3.6651375
Facet: time: 0.8072917 nDCG@5 = 0.2371977 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: year: 0.75 nDCG@5 = 0.2371977 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: month: 0.16666667 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.5
Facet: quarter: 0.13020833 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: company: 0.125 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: united: 0.104166664 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: business: 0.088541664 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: companies: 0.083333336 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: financial: 0.083333336 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: discover financial services: 0.041666668 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: services: 0.041666668 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = -0.11859885, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = -0.5, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 1.0 facet gains recall 0.5
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue goldman sachs
Average number of facet_Y values in each document is 2.2135417
Average number of documents tagged with each facet value in the facet_Y field is : 2.125
Facet: revenue: 0.328125 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.5
Facet: goldman sachs: 0.13020833 nDCG@5 = 0.2371977 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: sales: 0.119791664 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.5
Facet: apple inc: 0.083333336 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: mobile phone: 0.046875 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: automobile: 0.041666668 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: share finance: 0.036458332 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: profit accounting: 0.036458332 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: price: 0.03125 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: iphone: 0.03125 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0 Facet: toyota: 0.026041666 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.2371977
facet gain @5 = 0.0, facet loss @5 = 0.2371977 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = -0.35579655, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = -1.5, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 1.0 facet gains recall 0.5
For query 5.7.3, on either X or Y field, the maximum nDCG Gain @5 = -0.11859885, alpha = 1 (assume user know which axis to choose).
For query 5.7.3, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 5.7.3, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 4.11.3 What is the revenue of Apple ?
Qid: 5.7.4 How has there been a change in Goldman Sachs revenue since 1990 ?
Qid: 4.11.2 What is the total mobile revenue since the 90s ?
Qid: 1.8.3 What is the revenue trend of the company Ford since 2000 ?
Qid: 1.8.4 What company has the highest revenue : Ford , BMW , Toyota , or Honda ?
Qid: 4.1.2 How has Total Mobile revenue changed over the past twenty years ?
Qid: 4.5.1 How does Nokia , Apple , and Blackberry compare in terms of revenue ?
Qid: 4.5.2 What is the trend of the total mobile revenue over the past 20 years ?
Qid: 4.1.3 How has Apple revenue changed over the past twenty years ?
Qid: 2.12.2 How does the revenue for Visa and Discover compare to Mastercard and American Express ?
Qid: 5.15.1 Has Goldman Sachs been increasing in revenue over the last 15 years ?
Qid: 2.12.1 How has the revenue for credit card companies changed from 2007 to 2009 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue) AND Y:(credit card companies) AND X:(from 2007 to 2009) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 2007 2009
Average number of facet_X values in each document is 6.174107
Average number of documents tagged with each facet value in the facet_X field is : 2.9177215
Facet: time: 0.6339286 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.59375 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.23660715 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.16071428 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.12053572 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.116071425 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.10714286 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.09821428 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.071428575 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: production: 0.06696428 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: vehicles: 0.06696428 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 0.8 facet gains recall 0.8
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue credit card companies
Average number of facet_Y values in each document is 2.1517856
Average number of documents tagged with each facet value in the facet_Y field is : 2.1909091
Facet: revenue: 0.29464287 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.11160714 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: sales: 0.102678575 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.06696428 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.058035713 nDCG@5 = 0.21398626 nDCG@5 nDCG gain = 0.21398626 **
facet gain @5 = 0.21398626, facet loss @5 = 0.0 novel docs @5 = 1 Facet: automobile: 0.058035713 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.04464286 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.035714287 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.03125 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.026785715 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.02232143 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.6 facet gains recall 0.6
For query 2.12.1, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 2.12.1, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 2.12.1, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.15.2 Has Goldman Sachs been increasing in revenue over the last 20 years ?
Qid: 5.15.3 What is the revenue for Goldman Sachs ?
Qid: 2.16.2 How does Visa compare to other credit card companies ?
Qid: 2.16.1 How has the credit card industry changed over the last 5 years ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the credit card industry) AND X:(the last 5 years) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 5 years
Average number of facet_X values in each document is 4.882353
Average number of documents tagged with each facet value in the facet_X field is : 3.2442997
Facet: time: 0.75490195 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.7058824 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.16176471 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: month: 0.15686275 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.12254902 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.11764706 nDCG@5 = 0.1510196 nDCG@5 nDCG gain = 0.1510196 **
facet gain @5 = 0.1510196, facet loss @5 = 0.0 novel docs @5 = 1 Facet: financial: 0.09803922 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.083333336 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.05392157 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: american express: 0.04901961 nDCG@5 = 0.1510196 nDCG@5 nDCG gain = 0.1510196 **
facet gain @5 = 0.1510196, facet loss @5 = 0.0 novel docs @5 = 1 Facet: history: 0.04411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 0.25 facet gains recall 0.25
................. Analyzing Facet field facet_Y..................
Field facet_Y: credit card industry
Average number of facet_Y values in each document is 2.1421568
Average number of documents tagged with each facet value in the facet_Y field is : 2.0325582
Facet: revenue: 0.2647059 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: sales: 0.12254902 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.1127451 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.078431375 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.05392157 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.04411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.039215688 nDCG@5 = 0.5143372 nDCG@5 nDCG gain = 0.5143372 **
facet gain @5 = 0.5143372, facet loss @5 = 0.0 novel docs @5 = 3 Facet: profit accounting: 0.039215688 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.029411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.024509804 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.024509804 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.0 facet gains recall 0.0
For query 2.16.1, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 2.16.1, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 2.16.1, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 2.16.4 Does Visa or Mastercard make more money ?
Qid: 2.16.3 How does Mastercard compare to other credit card companies ?
Qid: 3.3.2 How has Avis revenue changed over the course of the past 15 years ?
Qid: 3.3.4 How does Avis revenue compare to other companies in the year 2010 ?
Qid: 5.10.1 How does revenue for Goldman sachs change from 1990 to 2010 ?
Qid: 5.10.2 How does revenue for Goldman sachs change from 1990 to 2000 ?
Qid: 5.10.3 What 5 year range is the revenue for Goldman Sachs the lowest ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: X:(5 year range) AND Y:(the revenue) AND Y:(Goldman Sachs) AND IMCategory:(Maximum-Minimum-Multiple)
................. Analyzing Facet field facet_X..................
Field facet_X: 5 year range
Average number of facet_X values in each document is 4.2331605
Average number of documents tagged with each facet value in the facet_X field is : 3.7136364
Facet: time: 0.8031088 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.746114 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: month: 0.1658031 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: quarter: 0.12953368 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: company: 0.12953368 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.103626944 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.0880829 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: discover financial services: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: services: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 1.0 facet gains recall 1.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue goldman sachs
Average number of facet_Y values in each document is 2.207254
Average number of documents tagged with each facet value in the facet_Y field is : 2.119403
Facet: revenue: 0.32642487 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.12953368 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: sales: 0.119170986 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.046632126 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.03626943 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.03626943 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.031088082 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.031088082 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.025906736 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.5 facet gains recall 0.5
For query 5.10.3, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 5.10.3, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 5.10.3, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.10.4 In what year is the revenue for Goldman Sachs is the lowest ?
Original Ranking nDCG@5 = 0.23770012
Original Recall@5 = 0.22222222
Processed query: X:(year) AND Y:(the revenue) AND Y:(Goldman Sachs) AND IMCategory:(Maximum-Minimum-Multiple)
................. Analyzing Facet field facet_X..................
Field facet_X: year
Average number of facet_X values in each document is 4.2331605
Average number of documents tagged with each facet value in the facet_X field is : 3.7136364
Facet: time: 0.8031088 nDCG@5 = 0.27805114 nDCG@5 nDCG gain = 0.04035102 **
facet gain @5 = 0.04035103, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 0.33333334 facet gains recall 0.11111112
Facet: year: 0.746114 nDCG@5 = 0.27805114 nDCG@5 nDCG gain = 0.04035102 **
facet gain @5 = 0.04035103, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 0.33333334 facet gains recall 0.11111112
Facet: month: 0.1658031 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.22222222
Facet: quarter: 0.12953368 nDCG@5 = 0.11677353 nDCG@5 nDCG gain = -0.12092659
facet gain @5 = 0.0264252, facet loss @5 = 0.14735179 novel docs @5 = 0 Facet: company: 0.12953368 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: united: 0.103626944 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: business: 0.0880829 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: companies: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: financial: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: discover financial services: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: services: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = -0.053040363, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0658097, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 1.1309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.6666667
facet_X, total recall @5 = 1.0 facet gains recall 0.7777778
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue goldman sachs
Average number of facet_Y values in each document is 2.207254
Average number of documents tagged with each facet value in the facet_Y field is : 2.119403
Facet: revenue: 0.32642487 nDCG@5 = 0.14735179 nDCG@5 nDCG gain = -0.09034833
facet gain @5 = 0.057003457, facet loss @5 = 0.14735179 novel docs @5 = 0 recall @5 = 0.11111111 facet gains recall -0.11111111
Facet: goldman sachs: 0.12953368 nDCG@5 = 0.1774679 nDCG@5 nDCG gain = -0.060232222
facet gain @5 = 0.030116111, facet loss @5 = 0.090348326 novel docs @5 = 1 recall @5 = 0.22222222 facet gains recall 0.0
Facet: sales: 0.119170986 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.22222222
Facet: apple inc: 0.08290155 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: mobile phone: 0.046632126 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: automobile: 0.041450776 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: share finance: 0.03626943 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: profit accounting: 0.03626943 nDCG@5 = 0.14735179 nDCG@5 nDCG gain = -0.09034833
facet gain @5 = 0.14735179, facet loss @5 = 0.23770012 novel docs @5 = 1 Facet: price: 0.031088082 nDCG@5 = 0.07784902 nDCG@5 nDCG gain = -0.1598511
facet gain @5 = 0.07784902, facet loss @5 = 0.23770012 novel docs @5 = 1 Facet: iphone: 0.031088082 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0 Facet: toyota: 0.025906736 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.23770012
facet gain @5 = 0.0, facet loss @5 = 0.23770012 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = -0.2472007, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.07600461, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = -2.1309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.33333334
facet_Y, total recall @5 = 0.7777778 facet gains recall 0.5555556
For query 5.10.4, on either X or Y field, the maximum nDCG Gain @5 = -0.053040363, alpha = 1 (assume user know which axis to choose).
For query 5.10.4, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.07600461, alpha = 0 (only consider documents covered by facets).
For query 5.10.4, on either X or Y field, the maximum binary nDCG Gain @5 = 1.1309297, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 2.1.2 How does the revenue earned by major credit card companies compare in 2010 ?
Qid: 5.14.1 What is the revenue of Goldman Sachs ?
Qid: 4.6.4 How does the revenue of Blackberry compare to that of other companies ?
Qid: 4.6.2 How has the total mobile revenue changed since the 1990 ?
Qid: 4.6.3 What is the revenue pattern change of Apple over the past 20 years ?
Qid: 1.7.1 How does Ford rank compared to other car dealers in terms of revenue ?
Qid: 1.7.2 How has Ford’s revenue changed over the last 12 years ?
Qid: 1.7.3 How has Ford’s revenue changed over the last 12 years ?
Qid: 4.11.4 How do the revenues of the companies Nokia , Apple , and Blackberry compare ?
Qid: 5.2.1 How does Goldman Sachs revenue in 1995 compare to that of 2010 ?
Qid: 5.2.2 How does Goldman Sachs revenue in 1990 compare to that of 2000 ?
Qid: 5.2.3 How does Goldman Sachs revenue in 2005 - 2010 rank among those of the past 15 years ?
Original Ranking nDCG@5 = 0.11861331
Original Recall@5 = 0.5
Processed query: Y:(Goldman Sachs revenue) AND X:(2010 rank) AND X:(those of the past 15 years) AND X:(in 2005 - 2010) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 2010 rank 15 years 2005 2010
Average number of facet_X values in each document is 4.740196
Average number of documents tagged with each facet value in the facet_X field is : 3.3460207
Facet: time: 0.75980395 nDCG@5 = 0.11861331 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: year: 0.7058824 nDCG@5 = 0.11861331 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: month: 0.15686275 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.5
Facet: company: 0.13725491 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: united: 0.12745099 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: quarter: 0.12254902 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: business: 0.0882353 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: financial: 0.0882353 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: companies: 0.078431375 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: history: 0.063725494 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: services: 0.04411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = -0.059306655, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = -0.5, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 1.0 facet gains recall 0.5
................. Analyzing Facet field facet_Y..................
Field facet_Y: goldman sachs revenue
Average number of facet_Y values in each document is 2.1813726
Average number of documents tagged with each facet value in the facet_Y field is : 2.1090047
Facet: revenue: 0.3137255 nDCG@5 = 0.3196312 nDCG@5 nDCG gain = 0.20101789 **
facet gain @5 = 0.3196312, facet loss @5 = 0.11861331 novel docs @5 = 1 recall @5 = 0.5 facet gains recall 0.0
Facet: sales: 0.12254902 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.5
Facet: goldman sachs: 0.12254902 nDCG@5 = 0.11861331 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.5 facet gains recall 0.0
Facet: apple inc: 0.078431375 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: mobile phone: 0.04411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: automobile: 0.039215688 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: share finance: 0.034313727 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: united: 0.034313727 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: profit accounting: 0.034313727 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: price: 0.029411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0 Facet: iphone: 0.029411765 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.11861331
facet gain @5 = 0.0, facet loss @5 = 0.11861331 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.12618122, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.3196312, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.36907023, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.33333334
facet_Y, total recall @5 = 1.0 facet gains recall 0.5
For query 5.2.3, on either X or Y field, the maximum nDCG Gain @5 = 0.12618122, alpha = 1 (assume user know which axis to choose).
For query 5.2.3, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.3196312, alpha = 0 (only consider documents covered by facets).
For query 5.2.3, on either X or Y field, the maximum binary nDCG Gain @5 = 0.36907023, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.2.4 How has Goldman Sachs revenue changed in the last 20 years ?
Qid: 5.6.1 What is the revenue growth of Goldman Sachs starting in 1995 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue growth) AND Y:(Goldman Sachs) AND X:(1995) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 1995
Average number of facet_X values in each document is 5.172727
Average number of documents tagged with each facet value in the facet_X field is : 3.010582
Facet: time: 0.6818182 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.6636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.19090909 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.19090909 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.14545454 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.14545454 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.13636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.13636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: history: 0.08181818 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: discover financial services: 0.07272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.07272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 0.0 facet gains recall 0.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue goldman sachs
Average number of facet_Y values in each document is 2.1545455
Average number of documents tagged with each facet value in the facet_Y field is : 2.2358491
Facet: revenue: 0.5 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.22727273 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.1 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: share finance: 0.045454547 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.045454547 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: net income: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: employment: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.027272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.027272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.027272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.0 facet gains recall 0.0
For query 5.6.1, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 5.6.1, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 5.6.1, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.6.2 What is the revenue growth of Goldman Sachs starting in 1990 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue growth) AND Y:(Goldman Sachs) AND X:(1990) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 1990
Average number of facet_X values in each document is 5.172727
Average number of documents tagged with each facet value in the facet_X field is : 3.010582
Facet: time: 0.6818182 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.6636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.19090909 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.19090909 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.14545454 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.14545454 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.13636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.13636364 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: history: 0.08181818 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: discover financial services: 0.07272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.07272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 0.0 facet gains recall 0.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue goldman sachs
Average number of facet_Y values in each document is 2.1363637
Average number of documents tagged with each facet value in the facet_Y field is : 2.2596154
Facet: revenue: 0.5 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.22727273 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.09090909 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: share finance: 0.045454547 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.045454547 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: net income: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: employment: 0.036363635 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.027272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.027272727 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.0 facet gains recall 0.0
For query 5.6.2, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 5.6.2, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 5.6.2, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.6.3 How has the revenue model of Goldman Sachs decreased over time ?
Qid: 5.6.4 What is the revenue model of Goldman Sachs over the past 20 years ?
Qid: 4.2.2 What is Apple’s revenue during the past decade ?
Qid: 4.2.3 What is Apple’s revenue during the past decade ?
Qid: 2.13.1 How does the revenue of credit card companies compare from 2008 to 2009 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue) AND Y:(credit card companies) AND IMCategory:(Relative-Difference)
................. Analyzing Facet field facet_X..................
Field facet_X:
Average number of facet_X values in each document is 6.1809955
Average number of documents tagged with each facet value in the facet_X field is : 2.8940678
Facet: time: 0.6334842 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.5927602 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.239819 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.15837105 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.11764706 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.11764706 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.108597286 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.09954751 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.07239819 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: production: 0.06787331 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: vehicles: 0.06787331 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 1.0 facet gains recall 1.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue credit card companies
Average number of facet_Y values in each document is 2.1674209
Average number of documents tagged with each facet value in the facet_Y field is : 2.1872146
Facet: revenue: 0.29864255 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.11312217 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: sales: 0.1040724 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.06787331 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.05882353 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.05882353 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.04524887 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.036199097 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.03167421 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.027149322 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.022624435 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.75 facet gains recall 0.75
For query 2.13.1, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 2.13.1, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 2.13.1, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 5.14.2 What is the revenue of the company Goldman Sachs ?
Qid: 5.14.3 What is the revenue of Goldman Sachs since the 90s ?
Qid: 2.13.3 How does the revenue of American Express in 2008 compare to Discover , Mastercard , and Visa during the same year ?
Qid: 5.14.4 How has the revenue of the company Goldman Sachs changed since the 90s ?
Qid: 2.13.2 How does the revenue of Discover compare to American Express in 2010 ?
Qid: 2.17.1 What is the revenue of the top credit card companies ?
Qid: 2.13.4 How does the revenue of Visa in 2007 compare to 2008 and 2009 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue) AND X:(Visa) AND Y:(to 2008 and 2009) AND X:(in 2007) AND IMCategory:(Relative-Difference) AND focusX:(Visa,in 2007)
................. Analyzing Facet field facet_X..................
Field facet_X: visa 2007
Average number of facet_X values in each document is 4.9931507
Average number of documents tagged with each facet value in the facet_X field is : 3.5217392
Facet: time: 0.6917808 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.6849315 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.20547946 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.1849315 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.1369863 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.11643836 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.11643836 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.09589041 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.06849315 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: american express: 0.061643835 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: history: 0.061643835 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 1.0 facet gains recall 1.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue 2008 2009
Average number of facet_Y values in each document is 2.171233
Average number of documents tagged with each facet value in the facet_Y field is : 2.006329
Facet: revenue: 0.4178082 nDCG@5 = 0.38685277 nDCG@5 nDCG gain = 0.38685277 **
facet gain @5 = 0.38685277, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 1.0 facet gains recall 1.0
Facet: goldman sachs: 0.0890411 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.07534247 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: share finance: 0.05479452 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.05479452 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.04109589 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: sales: 0.04109589 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: employment: 0.034246575 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.02739726 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: net income: 0.02739726 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.02739726 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.38685277, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.38685277, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 1.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.33333334
facet_Y, total recall @5 = 1.0 facet gains recall 1.0
For query 2.13.4, on either X or Y field, the maximum nDCG Gain @5 = 0.38685277, alpha = 1 (assume user know which axis to choose).
For query 2.13.4, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.38685277, alpha = 0 (only consider documents covered by facets).
For query 2.13.4, on either X or Y field, the maximum binary nDCG Gain @5 = 1.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 2.7.3 How do Visa , Mastercard , Discover and American Express relate in terms of revenue models ?
Qid: 2.17.2 In 2010 , what is the revenue of the top credit companies ?
Qid: 3.4.1 How does the revenue of HERTZ compare to AVIS in 1995 ?
Qid: 3.4.2 How has the revenue of AVIS changed from 1995 to 2010 ?
Qid: 3.4.3 How does the revenue of AVIS compare to HERTZ in 2000 ?
Qid: 3.4.4 How does AVIS revenue compare to HERTZ and Budget in 2010 ?
Qid: 3.5.4 How does the revenue of Avis compare to other companies in 2010 ?
Qid: 2.2.4 What credit card company made the most money in 2008 ?
Original Ranking nDCG@5 = 0.16036522
Original Recall@5 = 0.17391305
Processed query: X:(credit card company) AND Y:(the most money) AND IMCategory:(Maximum-Minimum-Multiple)
................. Analyzing Facet field facet_X..................
Field facet_X: credit card company
Average number of facet_X values in each document is 6.2798166
Average number of documents tagged with each facet value in the facet_X field is : 2.8700209
Facet: time: 0.6330275 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.17391305
Facet: year: 0.5917431 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.17391305
Facet: company: 0.24311927 nDCG@5 = 0.16036522 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.018743582, facet loss @5 = 0.018743582 novel docs @5 = 1 recall @5 = 0.17391305 facet gains recall 0.0
Facet: united: 0.16513762 nDCG@5 = 0.2330424 nDCG@5 nDCG gain = 0.07267718 **
facet gain @5 = 0.09142077, facet loss @5 = 0.018743582 novel docs @5 = 2 Facet: month: 0.119266056 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: financial: 0.119266056 nDCG@5 = 0.22879577 nDCG@5 nDCG gain = 0.06843054 **
facet gain @5 = 0.08717412, facet loss @5 = 0.018743582 novel docs @5 = 2 Facet: business: 0.110091746 nDCG@5 = 0.12411357 nDCG@5 nDCG gain = -0.036251657
facet gain @5 = 0.04509264, facet loss @5 = 0.081344314 novel docs @5 = 2 Facet: quarter: 0.09633028 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: companies: 0.07339449 nDCG@5 = 0.6608398 nDCG@5 nDCG gain = 0.5004746 **
facet gain @5 = 0.6608398, facet loss @5 = 0.16036522 novel docs @5 = 4 Facet: vehicles: 0.07339449 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: production: 0.06880734 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = -0.2615444, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.009371791, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = -1.6309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.33333334
facet_X, total recall @5 = 0.82608694 facet gains recall 0.6521739
................. Analyzing Facet field facet_Y..................
Field facet_Y: money
Average number of facet_Y values in each document is 2.1880734
Average number of documents tagged with each facet value in the facet_Y field is : 2.2083333
Facet: revenue: 0.3027523 nDCG@5 = 0.9250257 nDCG@5 nDCG gain = 0.7646605 **
facet gain @5 = 0.9250257, facet loss @5 = 0.16036522 novel docs @5 = 5 recall @5 = 0.2173913 facet gains recall 0.04347825
Facet: goldman sachs: 0.1146789 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.17391305
Facet: sales: 0.10550459 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 recall @5 = 0.0 facet gains recall -0.17391305
Facet: apple inc: 0.07339449 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: share finance: 0.059633028 nDCG@5 = 0.07267719 nDCG@5 nDCG gain = -0.087688036
facet gain @5 = 0.05393361, facet loss @5 = 0.14162165 novel docs @5 = 1 Facet: automobile: 0.059633028 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: mobile phone: 0.041284405 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: price: 0.03211009 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: profit accounting: 0.03211009 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: iphone: 0.027522936 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0 Facet: toyota: 0.02293578 nDCG@5 = 0.0 nDCG@5 nDCG gain = -0.16036522
facet gain @5 = 0.0, facet loss @5 = 0.16036522 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.5832987, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.9250257, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = -0.13092977, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 1.6666666
facet_Y, total recall @5 = 0.3478261 facet gains recall 0.17391305
For query 2.2.4, on either X or Y field, the maximum nDCG Gain @5 = 0.5832987, alpha = 1 (assume user know which axis to choose).
For query 2.2.4, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.9250257, alpha = 0 (only consider documents covered by facets).
For query 2.2.4, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 2.2.2 How do Visa and Discover compare in 2010 ?
Qid: 5.13.1 How much revenue did Goldman Sachs earn from 1995 to 2010 ?
Qid: 2.2.1 How has the revenue of the Credit Card Companies changed in the past two years ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue) AND Y:(the Credit Card Companies) AND X:(the past two years) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: years
Average number of facet_X values in each document is 5.987342
Average number of documents tagged with each facet value in the facet_X field is : 2.9748428
Facet: time: 0.65822786 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: year: 0.6075949 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: company: 0.22362868 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: united: 0.15189873 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: month: 0.13502109 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.10970464 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.10548523 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.101265825 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.067510545 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: production: 0.06329114 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: vehicles: 0.06329114 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 0.0
facet_X, total recall @5 = 0.625 facet gains recall 0.625
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue credit card companies
Average number of facet_Y values in each document is 2.1603374
Average number of documents tagged with each facet value in the facet_Y field is : 2.1603374
Facet: revenue: 0.278481 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: sales: 0.11814346 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.10548523 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.067510545 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.05485232 nDCG@5 = 0.44685355 nDCG@5 nDCG gain = 0.44685355 **
facet gain @5 = 0.44685355, facet loss @5 = 0.0 novel docs @5 = 3 Facet: automobile: 0.05485232 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.042194095 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.033755273 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.029535865 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.025316456 nDCG@5 = 0.21398626 nDCG@5 nDCG gain = 0.21398626 **
facet gain @5 = 0.21398626, facet loss @5 = 0.0 novel docs @5 = 1 Facet: iphone: 0.025316456 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.0, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.0, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 0.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.0
facet_Y, total recall @5 = 0.375 facet gains recall 0.375
For query 2.2.1, on either X or Y field, the maximum nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose).
For query 2.2.1, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.0, alpha = 0 (only consider documents covered by facets).
For query 2.2.1, on either X or Y field, the maximum binary nDCG Gain @5 = 0.0, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 4.7.1 How did Nokia , Apple , and Blackberry compare side by side ?
Qid: 1.4.2 What is the Ford revenue model every four years beginning in 2000 ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the Ford revenue model) AND X:(in 2000) AND X:(every four years) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: 2000 years
Average number of facet_X values in each document is 5.231884
Average number of documents tagged with each facet value in the facet_X field is : 3.0251396
Facet: time: 0.7536232 nDCG@5 = 0.3127697 nDCG@5 nDCG gain = 0.3127697 **
facet gain @5 = 0.3127697, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 0.33333334 facet gains recall 0.33333334
Facet: year: 0.6956522 nDCG@5 = 0.40641758 nDCG@5 nDCG gain = 0.40641758 **
facet gain @5 = 0.40641758, facet loss @5 = 0.0 novel docs @5 = 2 recall @5 = 0.6666667 facet gains recall 0.6666667
Facet: company: 0.16425121 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: month: 0.15458937 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: quarter: 0.12077295 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.10628019 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: vehicles: 0.077294685 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: production: 0.072463766 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.06763285 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.06763285 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: financial: 0.062801935 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.5691906, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.5691906, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 1.6309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 1.0
facet_X, total recall @5 = 1.0 facet gains recall 1.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: ford revenue model
Average number of facet_Y values in each document is 2.1545894
Average number of documents tagged with each facet value in the facet_Y field is : 2.1442308
Facet: revenue: 0.30917874 nDCG@5 = 0.36311436 nDCG@5 nDCG gain = 0.36311436 **
facet gain @5 = 0.36311436, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 0.33333334 facet gains recall 0.33333334
Facet: sales: 0.14009662 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: goldman sachs: 0.11111111 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.077294685 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.062801935 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.04347826 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.038647342 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.033816423 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.028985508 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.028985508 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.024154589 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.36311436, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.36311436, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 1.0, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 0.33333334
facet_Y, total recall @5 = 0.33333334 facet gains recall 0.33333334
For query 1.4.2, on either X or Y field, the maximum nDCG Gain @5 = 0.5691906, alpha = 1 (assume user know which axis to choose).
For query 1.4.2, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.5691906, alpha = 0 (only consider documents covered by facets).
For query 1.4.2, on either X or Y field, the maximum binary nDCG Gain @5 = 1.6309297, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 1.4.3 How has the revenue model of Ford changed over time ?
Original Ranking nDCG@5 = 0.0
Original Recall@5 = 0.0
Processed query: Y:(the revenue model) AND Y:(Ford) AND X:(time) AND IMCategory:(Trend)
................. Analyzing Facet field facet_X..................
Field facet_X: time
Average number of facet_X values in each document is 5.4009433
Average number of documents tagged with each facet value in the facet_X field is : 2.772397
Facet: time: 0.740566 nDCG@5 = 0.23284444 nDCG@5 nDCG gain = 0.23284444 **
facet gain @5 = 0.23284444, facet loss @5 = 0.0 novel docs @5 = 2 recall @5 = 0.4 facet gains recall 0.4
Facet: year: 0.6792453 nDCG@5 = 0.34014976 nDCG@5 nDCG gain = 0.34014976 **
facet gain @5 = 0.34014976, facet loss @5 = 0.0 novel docs @5 = 2 recall @5 = 0.4 facet gains recall 0.4
Facet: company: 0.16509435 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: month: 0.1509434 nDCG@5 = 0.15684879 nDCG@5 nDCG gain = 0.15684879 **
facet gain @5 = 0.15684879, facet loss @5 = 0.0 novel docs @5 = 2 Facet: quarter: 0.11792453 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: united: 0.0990566 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: vehicles: 0.0754717 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: business: 0.070754714 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: production: 0.070754714 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: companies: 0.06603774 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: engine: 0.056603774 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_X, discounted nDCG Gain @5 = 0.44745505, alpha = 1 (this measures difference between nDCG).
facet_X, discounted cumulative facet Gain @5 = 0.44745505, alpha = 0 (only considering documents covered by each facet value).
facet_X, discounted non-negative nDCG Gain @5 = 1.6309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_X, average novel documents per facet value @5 = 1.3333334
facet_X, total recall @5 = 1.0 facet gains recall 1.0
................. Analyzing Facet field facet_Y..................
Field facet_Y: revenue model ford
Average number of facet_Y values in each document is 2.1509433
Average number of documents tagged with each facet value in the facet_Y field is : 2.1209302
Facet: revenue: 0.3018868 nDCG@5 = 0.20803519 nDCG@5 nDCG gain = 0.20803519 **
facet gain @5 = 0.20803519, facet loss @5 = 0.0 novel docs @5 = 1 recall @5 = 0.2 facet gains recall 0.2
Facet: sales: 0.13679245 nDCG@5 = 0.12907565 nDCG@5 nDCG gain = 0.12907565 **
facet gain @5 = 0.12907565, facet loss @5 = 0.0 novel docs @5 = 2 recall @5 = 0.4 facet gains recall 0.4
Facet: goldman sachs: 0.11320755 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 recall @5 = 0.0 facet gains recall 0.0
Facet: apple inc: 0.0754717 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: automobile: 0.061320756 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: mobile phone: 0.04245283 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: profit accounting: 0.03773585 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: price: 0.03301887 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: share finance: 0.028301887 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: iphone: 0.028301887 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0 Facet: toyota: 0.023584906 nDCG@5 = 0.0 nDCG@5 nDCG gain = 0.0
facet gain @5 = 0.0, facet loss @5 = 0.0 novel docs @5 = 0
facet_Y, discounted nDCG Gain @5 = 0.28947285, alpha = 1 (this measures difference between nDCG).
facet_Y, discounted cumulative facet Gain @5 = 0.28947285, alpha = 0 (only considering documents covered by each facet value).
facet_Y, discounted non-negative nDCG Gain @5 = 1.6309297, alpha = 1 (assume users know the correct facet values to choose.)
facet_Y, average novel documents per facet value @5 = 1.0
facet_Y, total recall @5 = 0.6 facet gains recall 0.6
For query 1.4.3, on either X or Y field, the maximum nDCG Gain @5 = 0.44745505, alpha = 1 (assume user know which axis to choose).
For query 1.4.3, on either X or Y field, the maximum Discounted Cumulative Facet Gain @5 = 0.44745505, alpha = 0 (only consider documents covered by facets).
For query 1.4.3, on either X or Y field, the maximum binary nDCG Gain @5 = 1.6309297, alpha = 1 (assume user know which axis to choose, only consider non-negative nDCG.).
Qid: 1.4.4 How do Ford , BMW , Toyota , and Honda compare in terms of revenue ?
Qid: 1.8.1 What is the revenue of the top car manufacturing companies ?
Qid: 1.8.2 What is the revenue of Ford in 2000 , 2004 and 2008 ?
Qid: 5.1.1 During which five year periods did revenue earned by Goldman Sachs decrease in the past twenty years ?
Qid: 5.1.2 How does revenue earned by Goldman Sachs for each five year time period compare for the past twenty years ?
Original Ranking nDCG@5 = 0.14600019
Original Recall@5 = 0.16666667
Processed query: X:(revenue) AND Y:(Goldman Sachs) AND X:(for the past twenty years) AND X:(for each five year time period) AND IMCategory:(Relative-Difference) AND focusX:(revenue,for the past twenty years,for each five year time period)
................. Analyzing Facet field facet_X..................
Field facet_X: revenue twenty years five year time period
Average number of facet_X values in each document is 4.7438426
Average number of documents tagged with each facet value in the facet_X field is : 2.8575668
Facet: time: 0.773399 nDCG@5 = 0.19819884 nDCG@5 nDCG gain = 0.05219865 **