Green-ChangePath

pom.xml

@@ -20,6 +20,12 @@
           <artifactId>mahout-core</artifactId>
           <version>0.9</version>
       </dependency>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-simple</artifactId>
+          <version>1.7.5</version>
+          <scope>test</scope>
+      </dependency>
       <dependency>
           <groupId>junit</groupId>
           <artifactId>junit</artifactId>

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

@@ -0,0 +1,108 @@
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.*;
+import java.util.*;
+
+import com.google.common.collect.BiMap;
+import com.google.common.collect.HashBiMap;
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.common.FastByIDMap;
+import org.apache.mahout.cf.taste.impl.model.GenericPreference;
+import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
+import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender;
+import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
+import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
+import org.apache.mahout.cf.taste.model.DataModel;
+import org.apache.mahout.cf.taste.model.PreferenceArray;
+import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
+import org.apache.mahout.cf.taste.recommender.RecommendedItem;
+import org.apache.mahout.cf.taste.recommender.Recommender;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
+
+public class MovieRecommender {
+    private final String path;
+    private ArrayList<ArrayList<GenericPreference>> data;
+    private HashMap<String, Integer> userTraductor;
+    private BiMap<String, Integer> productTraductor;
+    private int userIntegerId = 0;
+    private int productIntegerId = 0;
+
+    public MovieRecommender(final String path) throws IOException {
+        this.path = path;
+        data = new ArrayList();
+        userTraductor = new HashMap();
+        productTraductor = HashBiMap.create();
+        BufferedReader reader = new BufferedReader(new FileReader(this.path));
+        String line = "";
+        String productCode = "";
+        String userCode = "";
+        while ((line = reader.readLine()) != null) {
+            if (line.trim().length() <= 0) continue;
+            String[] duo = line.split("/");
+            if (duo.length != 2) continue;
+            if (duo[0].equals("product")) {
+                String[] separator = duo[1].split(":");
+                productCode = separator[1].trim();
+                if (!productTraductor.containsKey(productCode)) {
+                    productTraductor.put(productCode, productIntegerId);
+                    productIntegerId++;
+                }
+            } else {
+                String[] separator = duo[1].split(":");
+                if (separator[0].equals("userId")) {
+                    userCode = separator[1].trim();
+                    if (!userTraductor.containsKey(userCode)) {
+                        userTraductor.put(userCode, userIntegerId);
+                        data.add(new ArrayList());
+                        userIntegerId++;
+                    }
+                } else if (separator[0].equals("score")) {
+                    data.get(userTraductor.get(userCode)).add(new GenericPreference(userTraductor.get(userCode),
+                            productTraductor.get(productCode), Float.valueOf(separator[1].trim())));
+                }
+            }
+        }
+        File newFile = new File("src\\resources\\mahoutData.csv");
+        newFile.createNewFile();
+        FileWriter myWriter = new FileWriter("src\\resources\\mahoutData.csv");
+        for (int i = 0; i < data.size(); i++) {
+            for (int j = 0; j < data.get(i).size(); j++) {
+                myWriter.write(data.get(i).get(j).getUserID() + "," +
+                        data.get(i).get(j).getItemID() + "," + data.get(i).get(j).getValue() + "\n");
+            }
+        }
+        myWriter.close();
+    }
+
+    public int getTotalReviews() {
+        int reviews = 0;
+        for (ArrayList<GenericPreference> userReviews: data) {
+            reviews += userReviews.size();
+        }
+        return reviews;
+    }
+
+    public int getTotalProducts() {
+        return productTraductor.size();
+    }
+
+    public int getTotalUsers() {
+        return userTraductor.size();
+    }
+
+    public List<String> getRecommendationsForUser(final String userCode) throws IOException, TasteException {
+        DataModel model = new FileDataModel(new File("src\\resources\\mahoutData.csv"));
+        UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new NearestNUserNeighborhood(getTotalUsers(), userSimilarity, model);
+        Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, userSimilarity);
+        Recommender cachingRecommender = new CachingRecommender(recommender);
+        int userId = userTraductor.get(userCode);
+        List<RecommendedItem> recommendations = cachingRecommender.recommend(userId, getTotalProducts());
+        List<String> l = new ArrayList();
+        for (RecommendedItem recommendation : recommendations) {
+            l.add(productTraductor.inverse().get((int) recommendation.getItemID()));
+        }
+        return l;
+    }
+}

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

@@ -15,7 +15,7 @@ public class MovieRecommenderTest {
     public void testDataInfo() throws IOException, TasteException {
         //download movies.txt.gz from 
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
+        MovieRecommender recommender = new MovieRecommender("src\\resources\\movies.csv");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());