Mahout recommendation returns empty set - mahout

Question

I am trying to run KnnItemBasedRecommender using sample data "intro.csv" using the below code, however I am getting empty set as result.
public static void main(String[] args) throws Exception {
DataModel model = NeuvidisData.convertToDataModel();
//RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {
#Override
public Recommender buildRecommender(DataModel model) {
ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
Optimizer optimizer = new ConjugateGradientOptimizer();
return new KnnItemBasedRecommender(model, similarity, optimizer, 2);
}
};
Recommender rec = recommenderBuilder.buildRecommender(model);
List<RecommendedItem> rcList = rec.recommend(1, 2);
for(RecommendedItem item:rcList)
{
System.out.println("item:");
System.out.println(item);
}
}
Can anybody help me?

Answer 1

Presumably because your data is too small or sparse to make recommendations for user 1 using this algorithm. Without the data it's hard to say.

Answer 2

The following code worked for me.
ItemSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
Optimizer optimizer = new ConjugateGradientOptimizer();
Recommender recommender = new KnnItemBasedRecommender(dataModel, similarity, optimizer, 5);
Used PearsonCorrelationSimilarity instead of LogLikelihoodSimilarity.
This solution may work for a specific set of data. So, this solution is based on your data set.