Codigo para las funciones del test.

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

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+package nearsoft.academy.bigdata.recommendation;
+
+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.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+
+public class MovieRecommender {
+
+    private int reviews;
+    private long users;
+    private int products;
+    public UserBasedRecommender recommender;
+    private HashMap<String, Integer> productTable = new HashMap();
+    private HashMap<String, Long> userTable = new HashMap();
+    private HashMap<Integer, String> iProductsTable = new HashMap();
+
+    public MovieRecommender(String txt) throws Exception {
+        readReviewsFile(txt);
+    }
+
+    private void readReviewsFile(String txt) throws Exception {
+        DataModel model;
+        UserSimilarity similarity;
+        UserNeighborhood neighborhood;
+
+        File file = new File(txt);
+        BufferedReader br = new BufferedReader(new FileReader(file));
+        FileWriter writer = new FileWriter("data.csv");
+        BufferedWriter bw = new BufferedWriter(writer);
+        String csv = "";
+
+        for (String line = br.readLine(); line != null; line = br.readLine()) {
+            if (line.startsWith("product/productId")) {
+                String productId = line.split(" ")[1];
+                this.reviews++;
+                if (this.productTable.containsKey(productId)) {
+                    csv = this.productTable.get(productId) + ",";
+                } else {
+                    this.productTable.put(productId, this.products);
+                    this.iProductsTable.put(this.products, productId);
+                    csv = this.products + ",";
+                    this.products++;
+                }
+            } else if (line.startsWith("review/userId")) {
+                String userId = line.split(" ")[1];
+                if (this.userTable.containsKey(userId)) {
+                    csv = this.userTable.get(userId) + "," + csv;
+                } else {
+                    this.userTable.put(userId, this.users);
+                    csv = this.users + "," + csv;
+                    this.users++;
+                }
+            } else if (line.startsWith("review/score")) {
+                String reviewScore = line.split(" ")[1];
+                csv += reviewScore + "\n";
+                bw.write(csv);
+            }
+        }
+        bw.close();
+        model = new FileDataModel(new File("data.csv"));
+        similarity = new PearsonCorrelationSimilarity(model);
+        neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        this.recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+    }
+
+    public int getTotalReviews() {
+        return this.reviews;
+    }
+
+    public int getTotalProducts() {
+        return this.products;
+    }
+
+    public long getTotalUsers() {
+        return this.users;
+    }
+
+    public List<String> getRecommendationsForUser(String user) throws Exception {
+        List<String> results = new ArrayList();
+
+        long id = userTable.get(user);
+        List<RecommendedItem> recommendations = recommender.recommend(id, 3);
+
+        for (RecommendedItem recommendation : recommendations) {
+            results.add(iProductsTable.get((int) recommendation.getItemID()));
+        }
+
+        return results;
+    }
+}