Movie Recommender solved (Gibran)

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

@@ -0,0 +1,139 @@
+/*
+    IMPORTANT NOTES:
+        The program uses TXT instead of .GZ, you have to unzip the info first.
+        The program saves the csv data in the same path as this file.
+*/
+
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.Scanner;
+import java.util.List;
+import java.util.ArrayList;
+
+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 {
+    // Variables
+    File file;
+    FileWriter csv_writer;
+    String review_pattern = "review/userId:";
+    String products_pattern = "product/productId";
+    String score_pattern = "review/score:";
+    List<String> users = new ArrayList<String>();
+    List<String> products = new ArrayList<String>();
+    int total_reviews = 0;
+    long total_time = 0;
+
+    // Constructor
+    public MovieRecommender(String file_path) throws IOException{
+        // Opens the file given by the path
+        file = new File(file_path);
+        // Creates the CSV file
+        csv_writer = new FileWriter("movies.csv");
+        // Creates temporal variables
+        String[] temp_string;
+        String[] temp_line = {"","",""};
+        // Create the stream and scanner to read the file
+        FileInputStream inputStream = new FileInputStream(file);
+        Scanner scanner = new Scanner(inputStream, "UTF-8");
+
+        // Starts the loop to read all the file
+        while (scanner.hasNextLine()) {
+            // Saves the current line
+            String line = scanner.nextLine();
+
+            // If there was a product id in the line
+            if (line.contains(products_pattern)){
+                // Split the values between the pattern and the id
+                temp_string = line.split(" ");
+                // If it's a new product 
+                if(products.contains(temp_string[1]) == false){
+                    products.add(temp_string[1]);
+                }
+                // Saves the product on an array to later add it in the csv
+                temp_line[1] = temp_string[1];
+            }
+            if(line.contains(review_pattern)){
+                // Split the values between the pattern and the id
+                temp_string = line.split(" ");
+                // If it's a new user
+                if(users.contains(temp_string[1]) == false){
+                    users.add(temp_string[1]);
+                }
+                // Saves the user id on an array to later add it in the csv
+                temp_line[0] = temp_string[1];
+                // We count a new review if there was a user
+                total_reviews += 1;
+            }
+            if (line.contains(score_pattern)){
+                // Split the values between the pattern and the review score
+                temp_string = line.split(" ");
+                // Saves the score on an array to later add it in the csv
+                temp_line[2] = temp_string[1];
+            }
+            // If there is no more info, another block-review start
+            if (line.isEmpty()){
+                // Get the postion of the user_id and product_id
+                Integer user_id = users.indexOf(temp_line[0]);
+                Integer product_id = products.indexOf(temp_line[1]);
+                // Convert the int position to string
+                String save_user_id = user_id.toString();
+                String save_product_id = product_id.toString();
+                // Saves the user_id, product_id, and review_score in the CSV
+                csv_writer.append(save_user_id + "," + save_product_id + "," + temp_line[2] + "\n");
+            }
+        }
+        // Close the scanner, CSV and free the buffer.
+        scanner.close();
+        csv_writer.flush();
+        csv_writer.close();
+    }
+
+	public int getTotalReviews(){
+        return this.total_reviews;
+    }
+
+    public int getTotalUsers(){
+        return users.size();
+    }
+
+    public int getTotalProducts(){
+        return products.size();
+    }
+
+    public List<String> getRecommendationsForUser(String user_id) throws TasteException, IOException{
+        List<String> final_recommendations = new ArrayList<String>();
+
+        // Creates the model, similarity, neighborhood and recommender from Mahout 
+        DataModel model = new FileDataModel(new File("movies.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        int index = users.indexOf(user_id); // Gets the user id of the CSV
+        // Retrieves the recommendations for that user
+        List<RecommendedItem> recommendations = recommender.recommend(index, 10000);
+
+        // Add the products alfa numeric id into the final_recommendations list
+        for (RecommendedItem recommendation : recommendations) {
+            String temp_recomendation = products.get((int)recommendation.getItemID());
+            final_recommendations.add(temp_recomendation);
+        }
+
+        return final_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("/path/to/movies.txt"); // Changed to use txt instead of .gz
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());