How to reproduce the generated results?

[Ermeimei]

I follow the instruction that initialization of the random number is set by the the 'rec.random.seed' configuration.But each time I run my program ,it gave different result. This is my code:

public static void recommendingTest() throws LibrecException {
// build data model
Configuration conf = new Configuration();
conf.set("dfs.data.dir", "data/movielens/ml-100k");
conf.set("data.input.path", "ratings.txt");
conf.set("rec.random.seed","1");
conf.set("rec.recommender.verbose","true");
TextDataModel dataModel = new TextDataModel(conf);
dataModel.buildDataModel();
// build recommender context
RecommenderContext context = new RecommenderContext(conf, dataModel);
// build similarity
conf.set("rec.recommender.similarity.key" ,"item");
conf.setBoolean("rec.recommender.isranking", true);
conf.setInt("rec.similarity.shrinkage", 10);
RecommenderSimilarity similarity = new CosineSimilarity();
similarity.buildSimilarityMatrix(dataModel);
context.setSimilarity(similarity);
// build recommender
conf.set("rec.neighbors.knn.number", "50");
Recommender recommender = new ItemKNNRecommender();
recommender.setContext(context);
// run recommender algorithm
recommender.recommend(context);
// evaluate the recommended result
RecommenderEvaluator ndcgEvaluator = new NormalizedDCGEvaluator();
ndcgEvaluator.setTopN(30);
double ndcgValue = recommender.evaluate(ndcgEvaluator);
System.out.println("ndcg:" + ndcgValue);
List recommendedItemList = recommender.getRecommendedList();
GenericRecommendedFilter filter = new GenericRecommendedFilter();
List userIdList = new ArrayList();
userIdList.add("196");
filter.setUserIdList(userIdList);
recommendedItemList = filter.filter(recommendedItemList);
Map<String,Double> propRec = new HashMap<String,Double>();
for(RecommendedItem item: recommendedItemList) {
propRec.put(item.getItemId(),item.getValue());
}
CommonMethods.sortDoubleMap(propRec);
}

Two different results are as follow:

ndcg:0.31936048384981075
50=25.283366556667033
100=24.944878127693837
181=24.567060984693313
98=24.55614612533019
127=24.50435505248202
1=24.46178325820936
174=24.345619275053057
172=24.326974665385197
258=24.294352559184205
210=24.226078418472728

ndcg:0.3323971752269902
50=23.576304545467604
100=23.1964687349119
181=23.01336420205977
174=23.01040673256511
79=22.454712075943636
98=22.427443453029632
204=22.39011405365638
210=22.299173539563625
56=22.297498696999842
237=22.283066885242206

Thanks!

[Ermeimei]

I find the solution.Thanks to Mr Hong. If you use RecommenderJob to conduct an algorithm by reading the configuration file, we can reproduce results by adding this (rec.random.seed=1) into the configuration file. If you specify the configurations in the program,you should add this code Randoms.seed(1L) in the program.