Movie Recommender exercise solved (Pablo Cano)

pom.xml

@@ -5,7 +5,20 @@
   <groupId>nearsoft.academy</groupId>
   <artifactId>big-data</artifactId>
   <version>1.0-SNAPSHOT</version>
-  <packaging>jar</packaging>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.1</version>
+                <configuration>
+                    <source>16</source>
+                    <target>16</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+    <packaging>jar</packaging>
 
   <name>big-data</name>
   <url>http://maven.apache.org</url>

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

@@ -0,0 +1,136 @@
+package nearsoft.academy.bigdata.recommendation;
+
+import java.io.*;
+import java.util.ArrayList;
+import java.util.Hashtable;
+import java.util.List;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+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 {
+    //Initialize variables
+    Hashtable<String, Integer> users;
+    Hashtable<String, Integer> products;
+    Hashtable<Integer, String> productById;
+    int totalUsers;
+    int totalProducts;
+    int totalReviews;
+    FileReader file;
+
+    //Constructor
+    public MovieRecommender(String pathFile) throws IOException {
+        this.totalUsers = 0;
+        this.totalProducts = 0;
+        this.totalReviews = 0;
+        this.file = new FileReader(pathFile);
+        this.users = new Hashtable<>();
+        this.products = new Hashtable<>();
+        this.productById = new Hashtable<>();
+
+        processFile();
+    }
+
+    public void processFile() throws IOException {
+
+        String userId = "";
+        String productId = "";
+        String reviewId = "";
+
+        FileWriter csvFile = new FileWriter("data.csv");
+        BufferedReader br = new BufferedReader(this.file);
+
+        //Patterns for search the respective data with Regular Expressions
+        Pattern usersRegex = Pattern.compile("review\\/userId: ([\\D\\d]+)");
+        Pattern reviewsRegex = Pattern.compile("review\\/score: ([0-9]+).([0-9]+)");
+        Pattern productsRegex = Pattern.compile("product\\/productId: ([\\D\\d]+)");
+
+        //Initialize a Matcher object for the conditions below
+        Matcher matcherProduct, matcherUser, matcherReview;
+        //Initialize a string value for the line in the text file
+        String Line;
+
+        while ((Line = br.readLine()) != null){
+
+            matcherProduct = productsRegex.matcher(Line);
+            matcherUser = usersRegex.matcher(Line);
+            matcherReview = reviewsRegex.matcher(Line);
+
+            if(matcherUser.matches()) {
+                userId = Line.split(" ")[1];
+
+                if (users.get(userId) == null) {
+                    this.totalUsers ++;
+                    users.put(userId, this.totalUsers);
+                }
+            }
+
+            if (matcherReview.matches()) {
+                reviewId = Line.split(" ")[1];
+                this.totalReviews++;
+            }
+
+            if (matcherProduct.matches()) {
+                productId = Line.split(" ")[1];
+
+                if (products.get(productId) == null) {
+                    this.totalProducts++;
+                    products.put(productId, this.totalProducts);
+                    productById.put(this.totalProducts, productId);
+                }
+
+            }
+
+            // This condition is for write the csv file when the userid, reviewId and product id has a value then
+            // we reassign the variables to null values
+            if (!userId.equals("") && !reviewId.equals("") && !productId.equals("")){
+                csvFile.write(users.get(userId) + "," + products.get(productId) + "," + reviewId + "\n");
+                userId = "";
+                productId = "";
+                reviewId = "";
+            }
+
+        }
+
+        csvFile.close();
+    }
+
+    public int getTotalReviews(){
+        return this.totalReviews;
+    }
+    public int getTotalProducts(){
+        return this.totalProducts;
+    }
+    public int getTotalUsers(){
+        return this.totalUsers;
+    }
+
+    public List<String> getRecommendationsForUser(String userID) throws IOException, TasteException {
+
+        DataModel model = new FileDataModel(new File("data.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        List<String> recommendations = new ArrayList<>();
+
+        //For the 3 items in recommender.recommend(...) we add the values in the index from the hashtable productById in
+        // the recommendations list
+        for (RecommendedItem recommendation : recommender.recommend(users.get(userID), 3)) {
+            recommendations.add(productById.get((int)(recommendation.getItemID())));
+        }
+
+        return recommendations;
+    }
+
+}

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

@@ -15,7 +15,8 @@ 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");
+        //Use a unzip file
+        MovieRecommender recommender = new MovieRecommender("movies.txt");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());