Implemented MovieRecommender class, All Junit tests passed

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java

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+
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+
+import com.google.common.collect.BiMap;
+import com.google.common.collect.HashBiMap;
+
+import org.apache.mahout.cf.taste.impl.model.GenericPreference;
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.common.*;
+import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray;
+import org.apache.mahout.cf.taste.impl.model.GenericDataModel;
+import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
+import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
+import org.apache.mahout.cf.taste.model.PreferenceArray;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
+import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
+import org.apache.mahout.cf.taste.recommender.RecommendedItem;
+
+public class MovieRecommender {
+
+    private FastByIDMap<PreferenceArray> usersPreferences;
+    private HashMap<String, Long> userIDs;
+    private BiMap<String, Long> productIDsBi;
+
+    private long userCount;
+    private long productCount;
+    private long reviewCount;
+    private GenericDataModel model;
+    private GenericUserBasedRecommender recommender;
+
+    public MovieRecommender(String filePath) throws TasteException {
+
+        productIDsBi = HashBiMap.create();
+        userIDs = new HashMap<String, Long>();
+        usersPreferences = new FastByIDMap<PreferenceArray>();
+        userCount = 0;
+        productCount = 0;
+        reviewCount = 0;
+
+        getPreference(filePath);
+        buildRecomender();
+
+    }
+
+    GenericPreference getPreference(String filePath) {
+        String productID = "";
+        String userID = "";
+        Float score = 0.0f;
+
+        try {
+            BufferedReader reader = new BufferedReader(new FileReader(filePath));
+            String line = reader.readLine();
+            while (line != null) {
+
+                if (line.startsWith("product/productId:"))
+                    productID = line.split(" ")[1];
+                if (line.startsWith("review/userId:"))
+                    userID = line.split(" ")[1];
+                if (line.startsWith("review/score:")) {
+
+                    score = Float.parseFloat(line.split(" ")[1]);
+                    addPreference(userID, productID, score);
+
+                }
+                line = reader.readLine();
+            }
+            reader.close();
+
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+        return null;
+    }
+
+    private void addPreference(String userID, String productID, float score) {
+
+        reviewCount++;
+        if (!userIDs.containsKey(userID))
+            userIDs.put(userID, ++userCount);
+
+        if (!productIDsBi.containsKey(productID))
+            productIDsBi.put(productID, ++productCount);
+
+        Long longUserID = userIDs.get(userID);
+        Long longProductID = productIDsBi.get(productID);
+
+        addPreferenceToUser((GenericUserPreferenceArray) usersPreferences.get(longUserID),new GenericPreference(longUserID, longProductID, score));
+
+    }
+    private void addPreferenceToUser(GenericUserPreferenceArray existentPreferences, GenericPreference newPreference){
+
+        ArrayList preferencesList = new ArrayList();
+
+        if (existentPreferences != null) {
+            for (Integer i = 0; i < existentPreferences.length(); i++)
+                preferencesList.add(existentPreferences.get(i));
+        }
+
+        preferencesList.add(newPreference);
+
+        usersPreferences.put(newPreference.getUserID(), new GenericUserPreferenceArray(preferencesList));
+
+    }
+
+
+    private void buildRecomender() throws TasteException {
+        model = new GenericDataModel(usersPreferences);
+
+            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+            UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+            recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+    }
+
+    public int getTotalReviews() {
+
+        return (int) reviewCount;
+    }
+
+    public int getTotalProducts() {
+        return model.getNumItems();
+    }
+
+    public int getTotalUsers() {
+        return model.getNumUsers();
+    }
+
+    public List<String> getRecommendationsForUser(String user) throws TasteException {
+
+        List<String> result = new ArrayList<String>();
+        long longUserID = userIDs.get(user);
+            List<RecommendedItem>recomendations =  recommender .recommend(longUserID,3);
+
+            for(RecommendedItem item: recomendations )
+            {
+                result.add((String)productIDsBi.inverse().get(item.getItemID()));
+            }
+        return result;
+    }
+}