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Building Recommendation Engines by Suresh Kumar Gorakala

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Evaluating user-based recommenders

In this section, we shall see the code for evaluating the user-based recommendations we built in the previous section:

package com.packtpub.mahout.recommenders; import java.io.File; import java.io.IOException; import org.apache.mahout.cf.taste.common.TasteException; import org.apache.mahout.cf.taste.eval.RecommenderBuilder; import org.apache.mahout.cf.taste.eval.RecommenderEvaluator; import org.apache.mahout.cf.taste.impl.eval.RMSRecommenderEvaluator; import org.apache.mahout.cf.taste.impl.model.file.FileDataModel; import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood; import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender; import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity; ...

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