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MovieRecommender #21

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148 changes: 148 additions & 0 deletions MovieRecommender.java
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Original file line number Diff line number Diff line change
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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.Hashtable;
import java.util.List;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.zip.GZIPInputStream;

public class MovieRecommender {

int totalReviews;
int totalProducts;
int totalUsers;

//Storage hashtable AQUI: cambiar de strings a int
Hashtable<String, Integer> products = new Hashtable<String, Integer>();
Hashtable<String, Integer> users = new Hashtable<String, Integer>() ;



MovieRecommender(String path) throws IOException {
FileInputStream fileIn = new FileInputStream(path);
GZIPInputStream gZIPInputStream = new GZIPInputStream(fileIn);
Reader decoder = new InputStreamReader(gZIPInputStream);
BufferedReader reader = new BufferedReader(decoder);

String ln;

Pattern productsPattern = Pattern.compile("product\\/productId: ([\\D\\d]+)");
Pattern userPattern = Pattern.compile("review\\/userId: ([\\D\\d]+)");
Pattern scorePattern = Pattern.compile("review\\/score: ([\\D\\d]+)");


Matcher match;

this.totalReviews = 0;
this.totalProducts = 0;
this.totalUsers = 0;

boolean matches;

ln = reader.readLine();

String productId = "";
String userId = "";
String score = "";


FileWriter writer = new FileWriter("movies.csv");

while (ln != null ){
//codigo para procesar el archivo

//compara linea completa
match = userPattern.matcher(ln);
if (match.matches()) {
userId = ln.split(" ")[1];
this.totalReviews++;

if (users.get(userId) == null) {
this.totalUsers++;
users.put(userId, this.totalUsers);
}
}

match= productsPattern.matcher(ln);
if (match.matches()){
productId = ln.split(" ")[1];

if (products.get(productId) == null) {
this.totalProducts++;
products.put(productId, this.totalProducts);
}
}
match= scorePattern.matcher(ln);
if (match.matches()){
score = match.group(1);
//score = ln.split("")[1];
}

//user,product,score
if (!userId.equals("") && !productId.equals("") && !score.equals("")) {
writer.write(users.get(userId) + "," + products.get(productId) + "," + score + "\n");

userId = "";
productId = "";
score = "";
}

ln = reader.readLine();

}
writer.close();
reader.close();
gZIPInputStream.close();

}
public int getTotalReviews(){
return this.totalReviews;
}

public int getTotalProducts(){
return this.totalProducts;
}

public int getTotalUsers(){
return this.totalUsers;
}
public List<String> getRecommendationsForUser(String Id) throws IOException, TasteException {
//Creates the recommender (.csv)
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);

Integer userId = this.users.get(Id);

List<String> recom = new ArrayList<String>();

List <RecommendedItem> recommendations = recommender.recommend(userId, 3);
for (RecommendedItem recommendation : recommendations) {
int value = (int)recommendation.getItemID();
for (String key : products.keySet()) {
if (products.get(key)==value) {
recom.add(key);
}
}
}

return recom;
}
}
148 changes: 148 additions & 0 deletions src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java
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Open in desktop
Original file line number Diff line number Diff line change
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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.Hashtable;
import java.util.List;
import java.util.regex.Matcher;
import java.util.regex.Pattern;
import java.util.zip.GZIPInputStream;

public class MovieRecommender {

int totalReviews;
int totalProducts;
int totalUsers;

//Storage hashtable AQUI: cambiar de strings a int
Hashtable<String, Integer> products = new Hashtable<String, Integer>();
Hashtable<String, Integer> users = new Hashtable<String, Integer>() ;



MovieRecommender(String path) throws IOException {
FileInputStream fileIn = new FileInputStream(path);
GZIPInputStream gZIPInputStream = new GZIPInputStream(fileIn);
Reader decoder = new InputStreamReader(gZIPInputStream);
BufferedReader reader = new BufferedReader(decoder);

String ln;

Pattern productsPattern = Pattern.compile("product\\/productId: ([\\D\\d]+)");
Pattern userPattern = Pattern.compile("review\\/userId: ([\\D\\d]+)");
Pattern scorePattern = Pattern.compile("review\\/score: ([\\D\\d]+)");


Matcher match;

this.totalReviews = 0;
this.totalProducts = 0;
this.totalUsers = 0;

boolean matches;

ln = reader.readLine();

String productId = "";
String userId = "";
String score = "";


FileWriter writer = new FileWriter("movies.csv");

while (ln != null ){
//codigo para procesar el archivo

//compara linea completa
match = userPattern.matcher(ln);
if (match.matches()) {
userId = ln.split(" ")[1];
this.totalReviews++;

if (users.get(userId) == null) {
this.totalUsers++;
users.put(userId, this.totalUsers);
}
}

match= productsPattern.matcher(ln);
if (match.matches()){
productId = ln.split(" ")[1];

if (products.get(productId) == null) {
this.totalProducts++;
products.put(productId, this.totalProducts);
}
}
match= scorePattern.matcher(ln);
if (match.matches()){
score = match.group(1);
//score = ln.split("")[1];
}

//user,product,score
if (!userId.equals("") && !productId.equals("") && !score.equals("")) {
writer.write(users.get(userId) + "," + products.get(productId) + "," + score + "\n");

userId = "";
productId = "";
score = "";
}

ln = reader.readLine();

}
writer.close();
reader.close();
gZIPInputStream.close();

}
public int getTotalReviews(){
return this.totalReviews;
}

public int getTotalProducts(){
return this.totalProducts;
}

public int getTotalUsers(){
return this.totalUsers;
}
public List<String> getRecommendationsForUser(String Id) throws IOException, TasteException {
//Creates the recommender (.csv)
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);

Integer userId = this.users.get(Id);

List<String> recom = new ArrayList<String>();

List <RecommendedItem> recommendations = recommender.recommend(userId, 3);
for (RecommendedItem recommendation : recommendations) {
int value = (int)recommendation.getItemID();
for (String key : products.keySet()) {
if (products.get(key)==value) {
recom.add(key);
}
}
}

return recom;
}
}