Création d'interface de recommandation et de 4 implémentations

+package org.legrog.recommendation.process;
+
+import org.grouplens.lenskit.GlobalItemScorer;
+import org.grouplens.lenskit.ItemRecommender;
+import org.grouplens.lenskit.ItemScorer;
+import org.grouplens.lenskit.RecommenderBuildException;
+import org.grouplens.lenskit.baseline.BaselineScorer;
+import org.grouplens.lenskit.core.LenskitConfiguration;
+import org.grouplens.lenskit.core.LenskitRecommender;
+import org.grouplens.lenskit.data.dao.EventDAO;
+import org.grouplens.lenskit.data.history.LikeCountUserHistorySummarizer;
+import org.grouplens.lenskit.data.history.UserHistorySummarizer;
+import org.grouplens.lenskit.data.pref.PreferenceDomain;
+import org.grouplens.lenskit.data.text.DelimitedColumnEventFormat;
+import org.grouplens.lenskit.data.text.Fields;
+import org.grouplens.lenskit.data.text.LikeEventType;
+import org.grouplens.lenskit.data.text.TextEventDAO;
+import org.grouplens.lenskit.knn.item.ItemItemGlobalScorer;
+import org.grouplens.lenskit.knn.item.ItemItemScorer;
+import org.grouplens.lenskit.knn.item.ItemSimilarity;
+import org.grouplens.lenskit.knn.item.ItemVectorSimilarity;
+import org.grouplens.lenskit.scored.ScoredId;
+import org.grouplens.lenskit.vectors.similarity.CosineVectorSimilarity;
+import org.grouplens.lenskit.vectors.similarity.VectorSimilarity;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+import java.util.List;
+
+public class CosineRecommender implements Recommender {
+
+    Logger logger = LoggerFactory.getLogger(getClass());
+
+    LenskitConfiguration config;
+    DelimitedColumnEventFormat delimitedColumnEventFormat;
+
+    ItemRecommender irec;
+
+    @Override
+    public void Recommender(String filePath) {
+        config = new LenskitConfiguration();
+
+        config.bind(ItemScorer.class).to(ItemItemScorer.class);
+        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
+        config.bind(BaselineScorer.class, ItemScorer.class).to(ItemItemScorer.class);
+        config.bind(PreferenceDomain.class).to(new PreferenceDomain(0, 1));
+        config.bind(ItemSimilarity.class).to(ItemVectorSimilarity.class);
+        config.bind(VectorSimilarity.class).to(CosineVectorSimilarity.class);
+        config.bind(UserHistorySummarizer.class).to(LikeCountUserHistorySummarizer.class);
+
+        delimitedColumnEventFormat = new DelimitedColumnEventFormat(new LikeEventType());
+        delimitedColumnEventFormat.setDelimiter("\t");
+        delimitedColumnEventFormat.setHeaderLines(1);
+        delimitedColumnEventFormat.setFields(Fields.item(), Fields.user());
+
+        TextEventDAO textEventDAO = new TextEventDAO(new File(filePath), delimitedColumnEventFormat);
+        config.bind(EventDAO.class).to(textEventDAO);
+
+        LenskitRecommender rec;
+
+        try {
+            rec = LenskitRecommender.build(config);
+            irec = rec.getItemRecommender();
+        } catch (RecommenderBuildException e) {
+            logger.error("Recommender RecommenderBuildException {}", e.getStackTrace());
+        }
+    }
+
+    @Override
+    public List<ScoredId> recommend(long user, int n) {
+        return irec.recommend(user, n);
+    }
+}

+package org.legrog.recommendation.process;
+
+import org.grouplens.lenskit.GlobalItemScorer;
+import org.grouplens.lenskit.ItemRecommender;
+import org.grouplens.lenskit.ItemScorer;
+import org.grouplens.lenskit.RecommenderBuildException;
+import org.grouplens.lenskit.baseline.BaselineScorer;
+import org.grouplens.lenskit.core.LenskitConfiguration;
+import org.grouplens.lenskit.core.LenskitRecommender;
+import org.grouplens.lenskit.data.dao.EventDAO;
+import org.grouplens.lenskit.data.history.RatingVectorUserHistorySummarizer;
+import org.grouplens.lenskit.data.history.UserHistorySummarizer;
+import org.grouplens.lenskit.data.pref.PreferenceDomain;
+import org.grouplens.lenskit.data.text.DelimitedColumnEventFormat;
+import org.grouplens.lenskit.data.text.Fields;
+import org.grouplens.lenskit.data.text.RatingEventType;
+import org.grouplens.lenskit.data.text.TextEventDAO;
+import org.grouplens.lenskit.knn.item.ItemItemGlobalScorer;
+import org.grouplens.lenskit.mf.funksvd.FunkSVDItemScorer;
+import org.grouplens.lenskit.scored.ScoredId;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+import java.util.List;
+
+public class FunkSVDRecommender implements Recommender {
+
+    Logger logger = LoggerFactory.getLogger(getClass());
+
+    LenskitConfiguration config;
+    DelimitedColumnEventFormat delimitedColumnEventFormat;
+
+    ItemRecommender irec;
+
+    @Override
+    public void Recommender(String filePath) {
+        config = new LenskitConfiguration();
+
+        config.bind(ItemScorer.class).to(FunkSVDItemScorer.class);
+        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
+        config.bind(BaselineScorer.class, ItemScorer.class).to(FunkSVDItemScorer.class);
+        config.bind(PreferenceDomain.class).to(new PreferenceDomain(1.0, 5.0, 1.0));
+        config.bind(UserHistorySummarizer.class).to(RatingVectorUserHistorySummarizer.class);
+
+        delimitedColumnEventFormat = new DelimitedColumnEventFormat(new RatingEventType());
+        delimitedColumnEventFormat.setDelimiter("\t");
+        delimitedColumnEventFormat.setHeaderLines(1);
+        delimitedColumnEventFormat.setFields(Fields.item(), Fields.user(), Fields.rating());
+
+        TextEventDAO textEventDAO = new TextEventDAO(new File(filePath), delimitedColumnEventFormat);
+        config.bind(EventDAO.class).to(textEventDAO);
+
+        LenskitRecommender rec;
+
+        try {
+            rec = LenskitRecommender.build(config);
+            irec = rec.getItemRecommender();
+        } catch (RecommenderBuildException e) {
+            logger.error("Recommender RecommenderBuildException {}", e.getStackTrace());
+        }
+    }
+
+    @Override
+    public List<ScoredId> recommend(long user, int n) {
+        return irec.recommend(user, n);
+    }
+}

+package org.legrog.recommendation.process;
+
+import org.grouplens.lenskit.GlobalItemScorer;
+import org.grouplens.lenskit.ItemRecommender;
+import org.grouplens.lenskit.ItemScorer;
+import org.grouplens.lenskit.RecommenderBuildException;
+import org.grouplens.lenskit.baseline.BaselineScorer;
+import org.grouplens.lenskit.core.LenskitConfiguration;
+import org.grouplens.lenskit.core.LenskitRecommender;
+import org.grouplens.lenskit.data.dao.EventDAO;
+import org.grouplens.lenskit.data.history.LikeCountUserHistorySummarizer;
+import org.grouplens.lenskit.data.history.UserHistorySummarizer;
+import org.grouplens.lenskit.data.pref.PreferenceDomain;
+import org.grouplens.lenskit.data.text.DelimitedColumnEventFormat;
+import org.grouplens.lenskit.data.text.Fields;
+import org.grouplens.lenskit.data.text.LikeEventType;
+import org.grouplens.lenskit.data.text.TextEventDAO;
+import org.grouplens.lenskit.knn.item.ItemItemGlobalScorer;
+import org.grouplens.lenskit.knn.item.ItemItemScorer;
+import org.grouplens.lenskit.knn.item.ItemSimilarity;
+import org.grouplens.lenskit.knn.item.ItemVectorSimilarity;
+import org.grouplens.lenskit.scored.ScoredId;
+import org.grouplens.lenskit.vectors.similarity.PearsonCorrelation;
+import org.grouplens.lenskit.vectors.similarity.VectorSimilarity;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+import java.util.List;
+
+public class PearsonRecommender implements Recommender {
+
+    Logger logger = LoggerFactory.getLogger(getClass());
+
+    LenskitConfiguration config;
+    DelimitedColumnEventFormat delimitedColumnEventFormat;
+
+    ItemRecommender irec;
+
+    @Override
+    public void Recommender(String filePath) {
+        config = new LenskitConfiguration();
+
+        config.bind(ItemScorer.class).to(ItemItemScorer.class);
+        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
+        config.bind(BaselineScorer.class, ItemScorer.class).to(ItemItemScorer.class);
+        config.bind(PreferenceDomain.class).to(new PreferenceDomain(0, 1));
+        config.bind(ItemSimilarity.class).to(ItemVectorSimilarity.class);
+        config.bind(VectorSimilarity.class).to(PearsonCorrelation.class);
+        config.bind(UserHistorySummarizer.class).to(LikeCountUserHistorySummarizer.class);
+
+        delimitedColumnEventFormat = new DelimitedColumnEventFormat(new LikeEventType());
+        delimitedColumnEventFormat.setDelimiter("\t");
+        delimitedColumnEventFormat.setHeaderLines(1);
+        delimitedColumnEventFormat.setFields(Fields.item(), Fields.user());
+
+        TextEventDAO textEventDAO = new TextEventDAO(new File(filePath), delimitedColumnEventFormat);
+        config.bind(EventDAO.class).to(textEventDAO);
+
+        LenskitRecommender rec;
+
+        try {
+            rec = LenskitRecommender.build(config);
+            irec = rec.getItemRecommender();
+        } catch (RecommenderBuildException e) {
+            logger.error("Recommender RecommenderBuildException {}", e.getStackTrace());
+        }
+    }
+
+    @Override
+    public List<ScoredId> recommend(long user, int n) {
+        return irec.recommend(user, n);
+    }
+}

+package org.legrog.recommendation.process;
+
+import org.grouplens.lenskit.scored.ScoredId;
+
+import java.util.List;
+
+public interface Recommender {
+
+    void Recommender(String filePath);
+    List<ScoredId> recommend(long user, int n);
+}

+package org.legrog.recommendation.process;
+
+import org.grouplens.lenskit.GlobalItemScorer;
+import org.grouplens.lenskit.ItemRecommender;
+import org.grouplens.lenskit.ItemScorer;
+import org.grouplens.lenskit.RecommenderBuildException;
+import org.grouplens.lenskit.baseline.BaselineScorer;
+import org.grouplens.lenskit.core.LenskitConfiguration;
+import org.grouplens.lenskit.core.LenskitRecommender;
+import org.grouplens.lenskit.data.dao.EventDAO;
+import org.grouplens.lenskit.data.history.RatingVectorUserHistorySummarizer;
+import org.grouplens.lenskit.data.history.UserHistorySummarizer;
+import org.grouplens.lenskit.data.pref.PreferenceDomain;
+import org.grouplens.lenskit.data.text.*;
+import org.grouplens.lenskit.knn.item.ItemItemGlobalScorer;
+import org.grouplens.lenskit.scored.ScoredId;
+import org.grouplens.lenskit.slopeone.SlopeOneItemScorer;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.File;
+import java.util.List;
+
+public class SlopeOneRecommender implements Recommender {
+
+    Logger logger = LoggerFactory.getLogger(getClass());
+
+    LenskitConfiguration config;
+    DelimitedColumnEventFormat delimitedColumnEventFormat;
+
+    ItemRecommender irec;
+
+    @Override
+    public void Recommender(String filePath) {
+        config = new LenskitConfiguration();
+
+        config.bind(ItemScorer.class).to(SlopeOneItemScorer.class);
+        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
+        config.bind(BaselineScorer.class, ItemScorer.class).to(SlopeOneItemScorer.class);
+        config.bind(PreferenceDomain.class).to(new PreferenceDomain(1.0, 5.0, 1.0));
+        config.bind(UserHistorySummarizer.class).to(RatingVectorUserHistorySummarizer.class);
+
+        delimitedColumnEventFormat = new DelimitedColumnEventFormat(new RatingEventType());
+        delimitedColumnEventFormat.setDelimiter("\t");
+        delimitedColumnEventFormat.setHeaderLines(1);
+        delimitedColumnEventFormat.setFields(Fields.item(), Fields.user(), Fields.rating());
+
+        TextEventDAO textEventDAO = new TextEventDAO(new File(filePath), delimitedColumnEventFormat);
+        config.bind(EventDAO.class).to(textEventDAO);
+
+        LenskitRecommender rec;
+
+        try {
+            rec = LenskitRecommender.build(config);
+            irec = rec.getItemRecommender();
+        } catch (RecommenderBuildException e) {
+            logger.error("Recommender RecommenderBuildException {}", e.getStackTrace());
+        }
+    }
+
+    @Override
+    public List<ScoredId> recommend(long user, int n) {
+        return irec.recommend(user, n);
+    }
+}