MovieRecommender class

.gitignore

@@ -0,0 +1,8 @@
+data/
+target/
+.DS_Store
+.classpath
+.project
+.settings/
+.vscode/
+src/.DS_Store

pom.xml

@@ -12,14 +12,16 @@
 
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    <maven.compiler.source>1.7</maven.compiler.source>
+    <maven.compiler.target>1.7</maven.compiler.target>
   </properties>
 
   <dependencies>
       <dependency>
-          <groupId>org.apache.mahout</groupId>
-          <artifactId>mahout-core</artifactId>
-          <version>0.9</version>
-      </dependency>
+        <groupId>org.apache.mahout</groupId>
+        <artifactId>mahout-mr</artifactId>
+        <version>0.13.0</version>
+    </dependency>
       <dependency>
           <groupId>junit</groupId>
           <artifactId>junit</artifactId>

readme.md

@@ -9,8 +9,9 @@ This repo contains several common big data exercises.
 
 ## Setup
 
-1. Install the  JDK 7.0
+1. Install the  JDK 17
 2. [Download & Install Maven](http://maven.apache.org/download.cgi)
+3. Add `movies.txt.gz` file to `/data` folder on root. 
 
 
 ## How to run tests

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

@@ -0,0 +1,160 @@
+package nearsoft.academy;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.zip.GZIPInputStream;
+
+import org.apache.log4j.BasicConfigurator;
+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 {
+    final String CSV_PATH = "data/dataset.csv";
+    int totalUsers = 0;
+    int totalProducts = 0;
+    int totalReviews = 0;
+
+    private Map<String, Integer> users= new HashMap<String, Integer>();
+    private Map<String, Integer> products = new HashMap<String, Integer>();
+    private Map<Long, String> productsReverse = new HashMap<Long, String>();
+    private UserBasedRecommender recommender;
+
+    public MovieRecommender(String datasetPath) {
+        BasicConfigurator.configure();
+        try {
+            BufferedReader file = this.readGZFile(datasetPath);
+            processFileData(file);
+            DataModel model = new FileDataModel(new File(CSV_PATH));
+            UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+            UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+            recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+        } catch (Exception e) {
+            System.err.println(e.getLocalizedMessage());
+        }
+
+        return;
+    }
+
+    private BufferedReader readGZFile(String datasetPath) throws IOException {
+        FileInputStream file = new FileInputStream(datasetPath);
+        GZIPInputStream gz = new GZIPInputStream(file);
+        InputStreamReader reader = new InputStreamReader(gz);
+        BufferedReader br = new BufferedReader(reader);
+        return br;
+    }
+
+    private void processFileData(BufferedReader file) throws IOException {
+        List<String> requiredFields = Arrays.asList("review/userId:", "product/productId:", "review/score:");
+
+        String user = null;
+        String product = null;
+        String review = null;
+        String data;
+        int dataCount = 0;
+
+        File csvFile = new File(CSV_PATH);
+
+        if (csvFile.exists()) {
+            csvFile.delete();
+        }
+
+        FileWriter fileWriter = new FileWriter(csvFile);
+
+        String line = file.readLine();
+        String[] split;
+        while (line != null) {
+            if (dataCount == 3) {
+                dataCount = 0;
+                data = this.users.get(user) + "," + this.products.get(product) + "," + review + "\n";
+                fileWriter.write(data);
+            }
+            split = line.split(" ");
+            if (requiredFields.contains(split[0])) {
+                switch (split[0]) {
+                    case "review/userId:":
+                        user = split[1];
+                        dataCount++;
+                        addUserCount(user);
+                        break;
+                    case "product/productId:":
+                        product = split[1];
+                        dataCount++;
+                        addProductCount(product);
+                        break;
+                    case "review/score:":
+                        review = split[1];
+                        dataCount++;
+                        this.totalReviews++;
+                        break;
+                }
+            }
+            line = file.readLine();
+        }
+        fileWriter.close();
+        return;
+    }
+
+    private void addUserCount(String user) {
+        if (!this.users.containsKey(user)) {
+            this.users.put(user, this.totalUsers);
+            this.totalUsers++;
+        }
+        return;
+    }
+
+    private void addProductCount(String product) {
+        if (!this.products.containsKey(product)) {
+            this.products.put(product, this.totalProducts);
+            this.productsReverse.put(Long.valueOf(this.totalProducts), product);
+            this.totalProducts++;
+        }
+        return;
+    }
+
+    public int getTotalReviews() {
+        return this.totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return this.products.size();
+    }
+
+    public int getTotalUsers() {
+        return this.users.size();
+    }
+
+    public List<String> getRecommendationsForUser(String user) {
+        List<RecommendedItem> rec;
+        List<String> res = new ArrayList<String>();
+        String product;
+        try {
+            rec = recommender.recommend(this.users.get(user), 3);
+        } catch (Exception e) {
+            System.err.println(e.getLocalizedMessage());
+            return null;
+        }
+
+        for (RecommendedItem recommendation : rec) {
+            product = this.productsReverse.get(recommendation.getItemID());
+            res.add(product);
+        }
+
+        return res;
+    }
+}

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

@@ -3,6 +3,8 @@
 import org.apache.mahout.cf.taste.common.TasteException;
 import org.junit.Test;
 
+import nearsoft.academy.MovieRecommender;
+
 import java.io.IOException;
 import java.util.List;
 
@@ -15,7 +17,7 @@ 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("data/movies.txt.gz");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());