fix

nonpers-assignment/src/main/java/org/lenskit/mooc/nonpers/assoc/AssociationItemBasedItemRecommender.java

@@ -5,7 +5,6 @@ import org.lenskit.api.Result;
 import org.lenskit.api.ResultList;
 import org.lenskit.basic.AbstractItemBasedItemRecommender;
 import org.lenskit.results.Results;
-import org.lenskit.results.BasicResult;
 import org.lenskit.util.collections.LongUtils;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -91,7 +90,7 @@ public class AssociationItemBasedItemRecommender extends AbstractItemBasedItemRe
             Score entry = scoring.poll();
             if (entry == null) break;
 
-            results.add(new BasicResult(entry.id, entry.score));
+            results.add(Results.create(entry.id, entry.score));
         }
 
         return Results.newResultList(results);

nonpers-assignment/src/main/java/org/lenskit/mooc/nonpers/assoc/LiftAssociationModelProvider.java

@@ -14,6 +14,7 @@ import org.slf4j.LoggerFactory;
 import javax.inject.Inject;
 import javax.inject.Provider;
 import java.util.List;
+import java.util.HashSet;
 
 /**
  * Build an association rule model using a lift metric.
@@ -68,11 +69,21 @@ public class LiftAssociationModelProvider implements Provider<AssociationModel>
         for (Long2ObjectMap.Entry<LongSortedSet> xEntry: itemUsers.long2ObjectEntrySet()) {
             long xId = xEntry.getLongKey();
             LongSortedSet xUsers = xEntry.getValue();
+            HashSet xSet = new HashSet(xUsers);
 
             // set up a map to hold the scores for each 'y' item
             Long2DoubleMap itemScores = new Long2DoubleOpenHashMap();
 
             // TODO Compute lift association formulas for all other 'Y' items with respect to this 'X'
+            for (Long2ObjectMap.Entry<LongSortedSet> yEntry: itemUsers.long2ObjectEntrySet()) {
+                long yId = yEntry.getLongKey();
+                LongSortedSet yUsers = yEntry.getValue();
+
+                HashSet ySet = new HashSet(yUsers);
+                ySet.retainAll(xSet);
+                double p = 1.0 * ySet.size() * allUsers.size() / (xUsers.size() * yUsers.size());
+                itemScores.put(yId, p);
+            }
 
             // save the score map to the main map
             assocMatrix.put(xId, itemScores);

nonpers-assignment/src/main/java/org/lenskit/mooc/nonpers/mean/MeanItemBasedItemRecommender.java

@@ -15,6 +15,7 @@ import javax.inject.Inject;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Set;
+import java.util.PriorityQueue;
 
 /**
  * An item scorer that scores each item with its mean rating.
@@ -85,7 +86,29 @@ public class MeanItemBasedItemRecommender extends AbstractItemBasedItemRecommend
     private ResultList recommendItems(int n, LongSet items) {
         List<Result> results = new ArrayList<>();
 
-        // TODO Find the top N items by mean rating
+        class Score implements Comparable<Score> {
+            private double score;
+            private long id;
+            public Score(double score, long id) { this.score = score; this.id = id; }
+
+            @Override
+            public int compareTo(Score other) {
+                return new Double(-this.score).compareTo(new Double(-other.score));
+            }
+        }
+
+        PriorityQueue<Score> scoring = new PriorityQueue();
+        for (long item : items) {
+            double score = model.getMeanRating(item);
+            scoring.offer(new Score(score, item));
+        }
+
+        for (int i = 0; i < n; ++i) {
+            Score entry = scoring.poll();
+            if (entry == null) break;
+
+            results.add(Results.create(entry.id, entry.score));
+        }
 
         return Results.newResultList(results);
     }