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Recommender Systems with Surprise

Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.

Examples

1. RS with KNN
  • Model built from a plain text file
  • The algorithm used is: KNNBasic
  • Model trained using the technique of cross validation (5 folds)
  • The RMSE and MAE metrics were used to estimate the model error
  • Type of filtering: user-based collaborative
2. RS with SVD
  • Model built from a Pandas dataframe
  • The algorithm used is: Singular Value Decomposition (SVD)
  • Model trained using train and test datasets (80/20)
  • The error of the model was estimated using the RMSE metric
  • Type of filtering: collaborative
3. Tune model (SVD)
  • Model tuning: manual
  • Model tuning: automatic
  • Compute precision@k and recall@k

Data

MovieLens datasets were collected by the GroupLens Research Project at the University of Minnesota.

This data set consists of:

  • 100,000 ratings (1-5) from 943 users on 1682 movies.
  • Each user has rated at least 20 movies.
  • Simple demographic info for the users (age, gender, occupation, zip)

Table format: u.data

user id item id rating timestamp
196 242 3 881250949
186 302 3 891717742
22 377 1 878887116
244 51 2 880606923
166 346 1 886397596

Table format: u.item

movie id movie title release date IMDb URL
1 Toy Story (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)
2 GoldenEye (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?GoldenEye%20(1995)
3 Four Rooms (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)
4 Get Shorty (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)
5 Copycat (1995) 01-Jan-1995 http://us.imdb.com/M/title-exact?Copycat%20(1995)

Table format: u.user

user id age gender occupation zip code
1 24 M technician 85711
2 53 F other 94043
3 23 M writer 32067
4 24 M technician 43537
5 33 F other 15213

You can see the original dataset here

Python Dependencies

  conda install -c conda-forge scikit-surprise

Contributing and Feedback

Any kind of feedback/criticism would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc...).

Authors

  • Created by Andrés Segura Tinoco
  • Created on May 23, 2019

License

This project is licensed under the terms of the MIT license.

Acknowledgments

I would like to show my gratitude to:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI = http://dx.doi.org/10.1145/2827872