MovieRecommender Class finished

pom.xml

@@ -1,7 +1,6 @@
 <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
   <modelVersion>4.0.0</modelVersion>
-
   <groupId>nearsoft.academy</groupId>
   <artifactId>big-data</artifactId>
   <version>1.0-SNAPSHOT</version>
@@ -12,8 +11,9 @@
 
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+      <maven.compiler.source>7</maven.compiler.source>
+      <maven.compiler.target>7</maven.compiler.target>
   </properties>
-
   <dependencies>
       <dependency>
           <groupId>org.apache.mahout</groupId>
@@ -26,5 +26,38 @@
           <version>4.7</version>
           <scope>test</scope>
       </dependency>
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-api</artifactId>
+          <version>1.8.0-beta2</version>
+      </dependency>
+
+      <dependency>
+          <groupId>org.slf4j</groupId>
+          <artifactId>slf4j-simple</artifactId>
+          <version>1.8.0-beta2</version>
+          <scope>test</scope>
+      </dependency>
   </dependencies>
+    <build>
+        <plugins>
+            <plugin>
+                <artifactId>maven-surefire-plugin</artifactId>
+                <configuration>
+                    <includes>
+                        <include>**/*_UT.java</include>
+                        <include>**/*_FT.java</include>
+                    </includes>
+                </configuration>
+            </plugin>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>7</source>
+                    <target>7</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
 </project>

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

@@ -0,0 +1,161 @@
+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.net.URL;
+import java.util.*;
+import java.nio.file.Files;
+
+public class MovieRecommender {
+
+    public File resource;
+    public File data;
+    private String pathToResources = "src/main/resources/data.txt";
+
+    public int totalReviews;
+    public long totalProducts;
+    public long totalUsers;
+
+    private HashMap<String,Long> userHash = new HashMap<String, Long>();
+    private HashMap<String, Long> productHash = new HashMap<String, Long>();
+    private HashMap<Long, String> productHashStringId = new HashMap<Long, String>();
+
+
+    public int getTotalReviews() {
+        return totalReviews;
+    }
+
+    public void setTotalReviews(int totalReviews) {
+        this.totalReviews = totalReviews;
+    }
+
+    public long getTotalProducts() {
+        return totalProducts;
+    }
+
+    public void setTotalProducts(int totalProducts) {
+        this.totalProducts = totalProducts;
+    }
+
+    public long getTotalUsers() {
+        return totalUsers;
+    }
+
+    public void setTotalUsers(int totalUsers) {
+        this.totalUsers = totalUsers;
+    }
+
+
+    public List getRecommendationsForUser(String user) throws TasteException, IOException {
+
+        DataModel model = new FileDataModel(data);
+
+        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(userHash.get(user), 3);
+        List<String> recommendationIds = new ArrayList<String>();
+
+        for (RecommendedItem recommendation : recommendations) {
+            long itemId = recommendation.getItemID();
+            recommendationIds.add(productHashStringId.get(itemId));
+        }
+
+        return recommendationIds;
+
+    }
+
+    private URL getURL(String filename){
+        ClassLoader classLoader = getClass().getClassLoader();
+        URL url = classLoader.getResource(filename);
+        return url;
+    }
+
+    private File getFileFromResources(String fileName) {
+        URL resource = getURL(fileName);
+        if (resource == null) {
+            throw new IllegalArgumentException("file is not found!");
+        } else {
+            return new File(resource.getFile());
+        }
+    }
+
+    public void setInfo() throws IOException {
+
+        if (resource != null) {
+            FileReader reader = new FileReader(resource);
+            BufferedReader br = new BufferedReader(reader);
+
+            data = new File(pathToResources);
+
+            Files.deleteIfExists(data.toPath());
+            FileWriter fw = new FileWriter(data,true);
+            BufferedWriter nbr = new BufferedWriter(fw);
+            PrintWriter pw = new PrintWriter(nbr);
+
+            String line;
+
+            String userId = "";
+            String productId = "";
+            String score = "";
+
+            while ((line = br.readLine()) != null) {
+
+                if (line.startsWith("product/productId")) {
+                    totalReviews++;
+                    productId = line.split(" ")[1];
+
+                    if (!productHash.containsKey(productId)) {
+                        totalProducts++;
+                        productHash.put(productId, totalProducts);
+                        productHashStringId.put(totalProducts,productId);
+                    }
+                }
+                if (line.startsWith("review/userId")) {
+                    userId = line.split(" ")[1];
+
+                    if (!userHash.containsKey(userId)) {
+                        totalUsers++;
+                        userHash.put(userId, totalUsers);
+                    }
+                }
+                if (line.startsWith("review/score:")) {
+                    score = line.split(" ")[1];
+                    pw.println(userHash.get(userId) + "," + productHash.get(productId) + "," + score);
+                }
+            }
+
+            totalProducts = productHash.size();
+            totalUsers = userHash.size();
+            br.close();
+            pw.close();
+        }
+    }
+
+    public MovieRecommender(String path) throws IOException, TasteException {
+        this.resource = getFileFromResources(path);
+        setInfo();
+    }
+
+    public static void main (String [] args) throws IOException, TasteException {
+        MovieRecommender movie = new MovieRecommender("movies.txt");
+
+        System.out.println("REVIEWS " + movie.totalReviews);
+        System.out.println("PRODUCTS " + movie.totalProducts);
+        System.out.println("USERS " + movie.totalUsers);
+
+
+    }
+}