Movie Recommender Zeus

pom.xml

@@ -6,12 +6,27 @@
   <artifactId>big-data</artifactId>
   <version>1.0-SNAPSHOT</version>
   <packaging>jar</packaging>
+  <build>
+    <plugins>
+      <plugin>
+        <version>3.1</version>
+          <groupId>org.apache.maven.plugins</groupId>
+          <artifactId>maven-compiler-plugin</artifactId>
+          <configuration>
+              <source>17</source>
+              <target>17</target>
+          </configuration>
+      </plugin>
+    </plugins>
+  </build>
 
   <name>big-data</name>
   <url>http://maven.apache.org</url>
 
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    <maven.compiler.target>17</maven.compiler.target>
+    <maven.compiler.source>17</maven.compiler.source>
   </properties>
 
   <dependencies>
@@ -26,5 +41,10 @@
           <version>4.7</version>
           <scope>test</scope>
       </dependency>
+      <dependency>
+          <groupId>org.apache.commons</groupId>
+          <artifactId>commons-collections4</artifactId>
+          <version>4.4</version>
+      </dependency>
   </dependencies>
 </project>

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

@@ -0,0 +1,121 @@
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.*;
+import java.util.*;
+import java.nio.charset.StandardCharsets;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.stream.Collectors;
+import java.util.stream.Stream;
+import org.apache.commons.collections4.BidiMap;
+import org.apache.commons.collections4.bidimap.DualHashBidiMap;
+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.UserBasedRecommender;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+
+public class MovieRecommender {
+
+  private int totalReviews = 0;
+  private int totalProducts = 0;
+  private int totalUsers = 0;
+
+  private Hashtable<String, Integer> users = new Hashtable<>();
+  private BidiMap<String, Integer> products = new DualHashBidiMap<>();
+
+  public MovieRecommender(String moviesPath) {
+    try {  
+
+      File file = new File(moviesPath);
+      FileReader fr = new FileReader(file); 
+      BufferedReader br = new BufferedReader(fr); 
+
+      File csvFile = new File("movies.csv");
+      FileWriter fw = new FileWriter(csvFile);
+
+      String line;  
+
+      String score = null;
+      String productId = null;
+      String userId = null;
+
+      while((line = br.readLine()) != null){  
+        if (line.startsWith("review/score")) {
+          score = line.split(" ")[1];
+          totalReviews++;
+        }
+        else if (line.startsWith("product/productId")) {
+          productId = line.split(" ")[1];
+          if (!products.containsKey(productId)) {
+            totalProducts++;
+            products.put(productId, totalProducts);
+          }
+        }
+        else if (line.startsWith("review/userId")) {
+          userId = line.split(" ")[1];
+          if (!users.containsKey(userId)) {
+            totalUsers++;
+            users.put(userId, totalUsers);
+          }
+        }
+        if (productId != null && userId != null && score != null) {
+
+          StringBuilder row = new StringBuilder();
+          row.append(String.valueOf(users.get(userId)) + "," 
+              + String.valueOf(products.get(productId)) + ","
+              + score);
+          row.append("\n");
+          fw.write(row.toString());
+
+          productId = null;
+          userId = null;
+          score = null;
+        }
+      }  
+      fr.close();
+      fw.close();
+    }  
+    catch(IOException e){  
+      e.printStackTrace();  
+    } 
+  }
+
+  public int getTotalReviews(){
+    return totalReviews;
+  }
+
+  public int getTotalProducts(){
+    return totalProducts;
+  }
+
+  public int getTotalUsers(){
+    return totalUsers;
+  }
+
+  public List<String> getRecommendationsForUser(String userId) {
+
+    List<String> recommendations = new ArrayList<>();
+
+    try {
+      File datasetFile = new File("movies.csv");
+      DataModel model = new FileDataModel(datasetFile);
+      UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+      UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+      UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+      recommendations = recommender.recommend(users.get(userId), 3)
+        .stream()
+        .map(item -> products.getKey((int) item.getItemID()))
+        .collect(Collectors.toList());
+    } 
+    catch (IOException | TasteException e) {
+      e.printStackTrace();
+    }
+    return recommendations;
+  }
+}

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