Solution for Movie Recommender 2019b

pom.xml

@@ -5,7 +5,19 @@
   <groupId>nearsoft.academy</groupId>
   <artifactId>big-data</artifactId>
   <version>1.0-SNAPSHOT</version>
-  <packaging>jar</packaging>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>7</source>
+                    <target>7</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+    <packaging>jar</packaging>
 
   <name>big-data</name>
   <url>http://maven.apache.org</url>
@@ -15,6 +27,16 @@
   </properties>
 
   <dependencies>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-api</artifactId>
+          <version>1.7.5</version>
+      </dependency>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-log4j12</artifactId>
+          <version>1.7.5</version>
+      </dependency>
       <dependency>
           <groupId>org.apache.mahout</groupId>
           <artifactId>mahout-core</artifactId>

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

@@ -0,0 +1,108 @@
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.*;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.*;
+import java.util.zip.GZIPInputStream;
+
+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;
+
+public class MovieRecommender {
+    private String file;
+    private Hashtable<String, Integer> HashProduct = new Hashtable<String, Integer>();
+    private Hashtable<Integer, String> InvertedHashProduct = new Hashtable<Integer, String>();
+    private Hashtable<String, Integer> HashUser = new Hashtable<String, Integer>();
+    private int users =0, products =0, reviews = 0;
+
+    public MovieRecommender(String file) throws IOException{
+        this.file = file;
+        getData();
+    }
+
+    public String getData() throws IOException {
+        int thisProduct =0, thisUser =0;
+        Files.deleteIfExists(Paths.get("Result.csv"));
+        File result = new File("Result.csv");
+        InputStream fileReader = new GZIPInputStream(new FileInputStream(this.file));
+        BufferedReader br = new BufferedReader(new InputStreamReader(fileReader));
+        FileWriter fileWriter = new FileWriter(result);
+        BufferedWriter bw = new BufferedWriter(fileWriter);
+        String line;
+        String[] sp;
+        String key, value;
+
+        while((line = br.readLine()) != null) {
+            if (line.length() >= 0) {
+                sp = line.split(" ");
+                key = sp[0];
+                if (key.equals("product/productId:")) {
+                    value = sp[1];
+                    if (!HashProduct.containsKey(value)){
+                        HashProduct.put(value,products);
+                        InvertedHashProduct.put(products,value);
+                        thisProduct = HashProduct.get(value);
+                        products++;
+                    }else{
+                        thisProduct = HashProduct.get(value);
+                    }
+                }else if (key.equals("review/userId:")){
+                    value = sp[1];
+                    if (!HashUser.containsKey(value)){
+                        HashUser.put(value, users);
+                        thisUser = HashUser.get(value);
+                        users++;
+                    }else{
+                        thisUser = HashUser.get(value);
+                    }
+                }else if (key.equals("review/score:")){
+                    String score = sp[1];
+                    bw.write(thisUser + "," + thisProduct + "," + score + "\n");
+                    reviews ++;
+                }
+            }
+        }
+        br.close();
+        bw.close();
+        return null;
+
+
+    }
+
+    public int getTotalReviews() {
+        return reviews;
+    }
+
+    public int getTotalProducts() {
+        return products;
+    }
+
+    public int getTotalUsers() {
+        return users;
+    }
+
+    public List<String> getRecommendationsForUser(String user) throws IOException, TasteException {
+        DataModel model = new FileDataModel(new File("Result.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        int userValue = HashUser.get(user);
+
+        List RecommendedProducts = new ArrayList<String>();
+        List<RecommendedItem> recommendations = recommender.recommend(userValue,3);
+        for (RecommendedItem recommendation : recommendations) {
+            RecommendedProducts.add(InvertedHashProduct.get((int)recommendation.getItemID()));
+        }
+        return RecommendedProducts;
+    }
+}

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

@@ -13,9 +13,10 @@
 public class MovieRecommenderTest {
     @Test
     public void testDataInfo() throws IOException, TasteException {
-        //download movies.txt.gz from 
+        //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("/Users/alonso/Documents/big-data-exercises/movies.txt.gz");
+
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());
@@ -27,4 +28,4 @@ public void testDataInfo() throws IOException, TasteException {
 
     }
 
-}
+}