movieRecommenderClass

pom.xml

@@ -10,6 +10,26 @@
   <name>big-data</name>
   <url>http://maven.apache.org</url>
 
+  <build>
+    <plugins>
+      <plugin>  
+          <groupId>org.apache.maven.plugins</groupId>  
+          <artifactId>maven-surefire-plugin</artifactId>  
+          <version>2.12.4</version>  
+          <configuration>  
+            <skipTests>false</skipTests>  
+          </configuration>  
+      </plugin>
+      <plugin>
+          <groupId>org.apache.maven.plugins</groupId>
+          <artifactId>maven-compiler-plugin</artifactId>
+          <configuration>
+              <source>11</source>
+              <target>11</target>
+          </configuration>
+      </plugin>  
+    </plugins>
+  </build>
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
   </properties>

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

@@ -0,0 +1,119 @@
+package nearsoft.academy.bigdata.recommendation;
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
+import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
+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.neighborhood.UserNeighborhood;
+import org.apache.mahout.cf.taste.recommender.RecommendedItem;
+import org.apache.mahout.cf.taste.recommender.UserBasedRecommender;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+import java.io.*;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+    // Declare variables
+    int totalReviews = 0;
+    int totalProducts = 0;
+    int totalUsers = 0;
+
+    HashMap<String, Integer> productsMap = new HashMap<String, Integer>();
+    HashMap<String, Integer> usersMap = new HashMap<String, Integer>();
+    HashMap<Integer, String> inverseProductsMap = new HashMap<Integer, String>();
+
+    // MovieRecommender receive a path that is the Dataset 
+    public MovieRecommender(String path) throws  IOException {
+
+
+        GZIPInputStream document = new GZIPInputStream(new FileInputStream(path));
+        BufferedReader bReader = new BufferedReader(new InputStreamReader(document));
+        String line;
+        File result = new File("newData.csv");
+        BufferedWriter bWriter = new BufferedWriter(new FileWriter(result));
+
+        String productListCSV = "";
+        String userListCSV = "";
+        String scoreListCSV = "";
+
+        int numProducts = 0;
+        int numUsers = 0;
+        int numReviews = 0;
+
+        while ((line = bReader.readLine()) != null){
+            if (line.startsWith("product/productId:")){
+                numReviews++;
+                String [] tempLine = line.split(" ");
+                String productId = tempLine[1];
+
+                if (!productsMap.containsKey(productId)){
+                    // Fill out 2 Hashes 
+                    productsMap.put(productId, numProducts);
+                    inverseProductsMap.put(numProducts,productId);
+                    numProducts++;
+                }
+                productListCSV = productsMap.get(productId).toString();
+
+            } else if (line.startsWith("review/userId:")) {
+
+                String [] tempLine = line.split(" ");
+                String userID = tempLine[1];
+
+                if (!usersMap.containsKey(userID)){
+                    usersMap.put(userID,numUsers++);
+                }
+                userListCSV = usersMap.get(userID).toString();
+
+
+            } else if (line.startsWith("review/score:")){
+                String [] tempLine = line.split(" ");
+                scoreListCSV = tempLine[1];
+
+                // Format in the document
+                bWriter.write(userListCSV + "," + productListCSV + "," + scoreListCSV + "\n");
+            }
+        }
+
+        bReader.close();
+        bWriter.close();
+
+        this.totalReviews = numReviews;
+        this.totalProducts = productsMap.size();
+        this.totalUsers = usersMap.size();
+
+    }
+
+    public int getTotalReviews() {
+        return this.totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return this.totalProducts;
+    }
+
+    public int getTotalUsers() {
+        return this.totalUsers;
+    }
+
+
+    public List<String> getRecommendationsForUser(String userId) throws IOException, TasteException {
+
+        List<String> results = new ArrayList<String>();
+
+        DataModel model = new FileDataModel(new File("newData.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        List<RecommendedItem> recommendations = recommender.recommend(usersMap.get(userId), 3);
+
+        for (RecommendedItem recommendation : recommendations) {
+            results.add(inverseProductsMap.get((int)recommendation.getItemID()));
+        }
+        return results;
+
+    }
+}

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("movies.txt.gz");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());