From 23e2d36e6397e84bee4496dd906e6a0c659f6a91 Mon Sep 17 00:00:00 2001
From: Carlos Fabian <carl.fab.g@gmail.com>
Date: Mon, 19 Oct 2020 23:39:30 -0500
Subject: [PATCH] done

---
 .../recommendation/MovieRecommender.java      | 137 ++++++++++++++++++
 1 file changed, 137 insertions(+)
 create mode 100644 src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java

diff --git a/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
new file mode 100644
index 0000000..e4975a8
--- /dev/null
+++ b/src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
@@ -0,0 +1,137 @@
+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.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+import java.util.zip.GZIPInputStream;
+
+public class MovieRecommender {
+    private int countReviews;
+    private int countUsers;
+    private int countProducts;
+
+    HashMap<String, Integer> HashProducts = new HashMap<>();
+    HashMap<String, Integer> HashUsers = new HashMap<>();
+
+    //constructor
+    public MovieRecommender(String s) throws IOException{
+        this.countReviews = 0;
+        this.countProducts = 0;
+        this.countUsers = 0;
+        String currentProduct;
+        String currentUser;
+
+        GZIPInputStream input = new GZIPInputStream(new FileInputStream(s));
+        Reader decompressor = new InputStreamReader(input);
+        BufferedReader reader = new BufferedReader(decompressor);
+
+        File MoviesCVS = new File("movies.csv");
+        FileWriter fileWriter = new FileWriter(MoviesCVS);
+        BufferedWriter bufferedWriter = new BufferedWriter(fileWriter);
+
+        Pattern PatternProduct = Pattern.compile("^product/productId:\\s([\\d\\D]*)$");
+        Pattern PatternUser = Pattern.compile("^review/userId:\\s([\\d\\D]*)$");
+        Pattern PatternReview = Pattern.compile("^review/score:\\s(\\d.\\d)$");
+
+        String currentLine;
+        while((currentLine = reader.readLine()) != null){
+
+            Matcher MatchProduct = PatternProduct.matcher(currentLine);
+            Matcher MatchUser = PatternUser.matcher(currentLine);
+            Matcher MatchReview = PatternReview.matcher(currentLine);
+
+            if(MatchProduct.matches()) {
+                this.countReviews ++;
+                String ProductID = MatchProduct.group(1);
+                if(!HashProducts.containsKey(ProductID)){
+                    this.countProducts ++;
+                    HashProducts.put(ProductID, this.countProducts);
+                }
+
+                currentProduct = HashProducts.get(ProductID).toString();
+
+                while(!MatchUser.matches()){
+                    currentLine = reader.readLine();
+                    MatchUser = PatternUser.matcher(currentLine);
+                }
+
+                String userID = MatchUser.group(1);
+                if(!HashUsers.containsKey(userID)){
+
+                    this.countUsers += 1;
+                    HashUsers.put(userID, this.countUsers);
+
+                }
+                currentUser = HashUsers.get(userID).toString();
+
+                while(!MatchReview.matches()){
+                    currentLine = reader.readLine();
+                    MatchReview = PatternReview.matcher(currentLine);
+                }
+
+                String Review = MatchReview.group(1);
+                bufferedWriter.write(currentUser + "," + currentProduct + "," + Review + "\n");
+            }
+        }
+
+        bufferedWriter.close();
+        fileWriter.close();
+        decompressor.close();
+        reader.close();
+    }
+
+    public int getTotalReviews(){
+        return this.countReviews;
+    }
+
+    public int getTotalProducts(){
+        return this.countProducts;
+    }
+
+    public int getTotalUsers(){
+        return this.countUsers;
+    }
+
+    private String getProductID(int ID) {
+        for (String key : HashProducts.keySet()) {
+            if (HashProducts.get(key)==ID) {
+                return key;
+            }
+        }
+        return null;
+    }
+
+    public List<String> getRecommendationsForUser(String user) throws IOException, TasteException {
+        List<String> recommendations = new ArrayList<>();
+
+        int userId = HashUsers.get(user);
+
+        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);
+
+        List<RecommendedItem> Recommendations = recommender.recommend(userId, 3);
+        for (RecommendedItem recommendation : Recommendations) {
+            int value = (int)recommendation.getItemID();
+            String productIdRecommendation = getProductID(value);
+            recommendations.add(productIdRecommendation);
+        }
+
+        return recommendations;
+    }
+}