commit f8a292a488f67899ba2ffd8460007e08b56d71f5
author paulli <paul@prediction.io> Fri May 01 16:51:51 2015 +0800
committer paulli <paul@prediction.io> Fri May 01 16:51:51 2015 +0800
tree ffe7fc70d237d0a60fb7c82f8e703dc1b8652580
parent 389243e46127ae625d37a248345e47c2d2eeffb1

result of similar items

src/main/java/org/template/recommendation/Algorithm.java

diff --git a/src/main/java/org/template/recommendation/Algorithm.java b/src/main/java/org/template/recommendation/Algorithm.java
index 42d20df..d348b39 100644
--- a/src/main/java/org/template/recommendation/Algorithm.java
+++ b/src/main/java/org/template/recommendation/Algorithm.java
@@ -121,7 +121,7 @@
             if (recentProductFeatures.isEmpty()) {
                 return new PredictedResult(mostPopularItems());
             } else {
-                return new PredictedResult(similarItems(recentProductFeatures));
+                return new PredictedResult(similarItems(recentProductFeatures, model.getProductFeatures(), model.getIndexItemMap(), query.getNumber()));
             }
         }
     }
@@ -177,20 +177,38 @@
             itemScores.add(new ItemScore(indexItemMap.get(longKey), score));
         }
 
-        Collections.sort(itemScores);
+        Collections.sort(itemScores, Collections.reverseOrder());
 
         return itemScores.subList(0, Math.min(number, itemScores.size()));
     }
 
-    private List<ItemScore> similarItems(List<double[]> recentProductFeatures) {
+    private List<ItemScore> similarItems(List<double[]> recentProductFeatures, Map<Object, double[]> productFeatures, Map<Long, String> indexItemMap, int number) {
+        Set<Object> productKeys = productFeatures.keySet();
+        List<ItemScore> itemScores = new ArrayList<>(productFeatures.size());
+        for (Object key : productKeys) {
+            long longKey = ((Integer)key).longValue();
+            double[] feature = productFeatures.get(key);
+            double similarity = 0.0;
+            for (double[] recentFeature : recentProductFeatures) {
+                similarity += cosineSimilarity(feature, recentFeature);
+            }
+            itemScores.add(new ItemScore(indexItemMap.get(longKey), similarity));
+        }
 
+        Collections.sort(itemScores, Collections.reverseOrder());
 
-
-        return Collections.emptyList();
+        return itemScores.subList(0, Math.min(number, itemScores.size()));
     }
 
     private List<ItemScore> mostPopularItems() {
         return Collections.emptyList();
     }
 
+    private double cosineSimilarity(double[] a, double[] b) {
+        DoubleMatrix matrixA = new DoubleMatrix(a);
+        DoubleMatrix matrixB = new DoubleMatrix(b);
+
+        return matrixA.dot(matrixB) / (matrixA.norm2() * matrixB.norm2());
+    }
+
 }