Approach for solving the problem

pom.xml

@@ -10,9 +10,11 @@
   <name>big-data</name>
   <url>http://maven.apache.org</url>
 
-  <properties>
-    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
-  </properties>
+    <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>

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

@@ -0,0 +1,154 @@
+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.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+    HashMap<String, Integer> users = new HashMap<String, Integer>();
+    HashMap<String, Integer> items = new HashMap<String, Integer>();
+    Integer totalReviews = 0;
+    DataModel model = null;
+    UserBasedRecommender recommender = null;
+    String[] itemCodes = new String[1000000];
+
+    public MovieRecommender(String s) throws Exception {
+        String ROOT_PATH = System.getProperty("user.dir");
+
+        File rawData = new File(ROOT_PATH + "/src/main/resources/" + s);
+        File csvData = new File(ROOT_PATH + "/src/main/resources/data.csv");
+        if (csvData.exists() && !csvData.isDirectory()) {
+            BufferedReader csvDataReader = new BufferedReader(new FileReader(ROOT_PATH + "/src/main/resources/data.csv"));
+            BufferedReader csvUsersReader = new BufferedReader(new FileReader(ROOT_PATH + "/src/main/resources/users.csv"));
+            BufferedReader csvItemsReader = new BufferedReader(new FileReader(ROOT_PATH + "/src/main/resources/items.csv"));
+            String row = null;
+            // Count the number of reviews
+            while ((row = csvDataReader.readLine()) != null) {
+                totalReviews++;
+            }
+            csvDataReader.close();
+            // Create the user hash map
+            while ((row = csvUsersReader.readLine()) != null) {
+                String[] data = row.split(",");
+                users.put(data[0],Integer.valueOf(data[1]));
+            }
+            csvUsersReader.close();
+            // Create the item hash map
+            while ((row = csvItemsReader.readLine()) != null) {
+                String[] data = row.split(",");
+                items.put(data[0], Integer.valueOf(data[1]));
+                itemCodes[Integer.valueOf(data[1])] = data[0];
+            }
+            csvItemsReader.close();
+        }
+        else if(rawData.exists() && !rawData.isDirectory()) {
+            // Read the file
+            InputStream fileStream = new FileInputStream(ROOT_PATH + "/src/main/resources/" + s);
+            InputStream gzipStream = new GZIPInputStream(fileStream);
+            Reader decoder = new InputStreamReader(gzipStream, "UTF-8");
+            BufferedReader buffered = new BufferedReader(decoder);
+
+            // Prepare the writer for the CSVs, we need 3
+            // data: file used by the mahout recommender
+            // users: file that maps each userId by their string ID
+            // products: file that maps each productId by their string ID
+            FileWriter csvDataWriter = new FileWriter(ROOT_PATH + "/src/main/resources/data.csv");
+            FileWriter csvUsersWriter = new FileWriter(ROOT_PATH + "/src/main/resources/users.csv");
+            FileWriter csvItemsWriter = new FileWriter(ROOT_PATH + "/src/main/resources/items.csv");
+
+            // Go line by line and construct each line for the recommender
+            String line = null;
+            int userCounter = 1;
+            int itemCounter = 1;
+            int itemId = 0;
+            int userId = 0;
+            while ((line = buffered.readLine()) != null) {
+                if (line.contains("product/productId")){
+                    line = line.replace("product/productId: ", "");
+                    if (items.get(line) == null){
+                        items.put(line,  itemCounter);
+                        itemCounter++;
+                    }
+                    itemId = items.get(line);
+                    itemCodes[itemId] = line;
+                }
+                else if (line.contains("review/userId")) {
+                    line = line.replace("review/userId: ", "");
+                    if (users.get(line) == null){
+                        users.put(line,  userCounter);
+                        userCounter++;
+                    }
+                    userId = users.get(line);
+                }
+                else if (line.contains("review/score")){
+                    line = line.replace("review/score: ", "");
+                    csvDataWriter.append(String.valueOf(userId) + "," + String.valueOf(itemId) + "," + line + "\n");
+                    totalReviews++;
+                }
+            }
+            // Close the data file
+            csvDataWriter.flush();
+            csvDataWriter.close();
+            // Create the user hash csv file
+            for (String key: users.keySet()) {
+                csvUsersWriter.append(key + "," + users.get(key) + "\n");
+            }
+            csvUsersWriter.flush();
+            csvUsersWriter.close();
+            // Create the item hash csv file
+            for (String key: items.keySet()) {
+                csvItemsWriter.append(key + "," + items.get(key) + "\n");
+            }
+            csvItemsWriter.flush();
+            csvItemsWriter.close();
+        }
+        else {
+            throw new Exception("No such file exists");
+        }
+
+        // With the file either being read or created, create the recommendation model
+        model = new FileDataModel(new File(ROOT_PATH + "/src/main/resources/data.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+    }
+
+    public int getTotalReviews()
+    {
+        return totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return items.size();
+    }
+
+    public int getTotalUsers() {
+        return users.size();
+    }
+
+    public List<String> getRecommendationsForUser(String userCode) throws TasteException {
+        int userId = users.get(userCode);
+        String[] recommendationIds = new String[3];
+        int i = 0;
+        List<RecommendedItem> recommendations = recommender.recommend(userId, 3);
+        for (RecommendedItem recommendation : recommendations) {
+            recommendationIds[i] = itemCodes[(int) recommendation.getItemID()];
+            i++;
+        }
+        List<String> top3 = Arrays.asList(recommendationIds);
+        return top3;
+    }
+}

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

@@ -12,14 +12,16 @@
 
 public class MovieRecommenderTest {
     @Test
-    public void testDataInfo() throws IOException, TasteException {
+    public void testDataInfo() throws Exception {
         //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());
 
+
         List<String> recommendations = recommender.getRecommendationsForUser("A141HP4LYPWMSR");
         assertThat(recommendations, hasItem("B0002O7Y8U"));
         assertThat(recommendations, hasItem("B00004CQTF"));