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spark-ml-recommender

Package provides java implementation of big-data recommend-er using Apache Spark

Feature

The recommender library contains implementation of correlation-based algorithms as well as latent factor analysis (e.g., content-based filtering and collaborative filtering)

  • Collaborative filtering recommender that predicts user's preference on unknown items using ALS-based gradient descent
  • Recommender that computes the correlation / similarity between items based on user preference using Pearson / Cosine / Jaccard correlation coefficient
  • Recommender that computes the correlation / similarity between users based on their preferences towards items using Pearson / Cosine / Jaccard correlation coefficient
  • Recommender that recommends a common friend to connect two persons who do not know each other but like to know each other (something similar to linkedIn connection)

Install

Add the following dependency to your POM file:

<dependency>
  <groupId>com.github.chen0040</groupId>
  <artifactId>spark-ml-recommender</artifactId>
  <version>1.0.5</version>
</dependency>

Usage

Predict missing ratings

The sample code below tries to predict the missing rating of [user, movie] as shown in the ratingTable below:

movie-recommender

JavaSparkContext context = SparkContextFactory.createSparkContext("testing-1");

RatingTable ratingTable =new RatingTable();
ratingTable.addRating("Love at last", "Alice", 5);
ratingTable.addRating("Remance forever", "Alice", 5);
ratingTable.addRating("Nonstop car chases", "Alice", 0);
ratingTable.addRating("Sword vs. karate", "Alice", 0);
ratingTable.addRating("Love at last", "Bob", 5);
ratingTable.addRating("Cute puppies of love", "Bob", 4);
ratingTable.addRating("Nonstop car chases", "Bob", 0);
ratingTable.addRating("Sword vs. karate", "Bob", 0);
ratingTable.addRating("Love at last", "Carol", 0);
ratingTable.addRating("Cute puppies of love", "Carol", 0);
ratingTable.addRating("Nonstop car chases", "Carol", 5);
ratingTable.addRating("Sword vs. karate", "Carol", 5);
ratingTable.addRating("Love at last", "Dave", 0);
ratingTable.addRating("Remance forever", "Dave", 0);
ratingTable.addRating("Nonstop car chases", "Dave", 4);

JavaRDD<UserItemRating> input = context.parallelize(ratingTable.getRatings());

CFRecommender recommender = new CFRecommender();
recommender.setMaxIterations(50);
recommender.setFeatureCount(2);

JavaRDD<UserItemRating> output = recommender.fitAndTransform(input);

List<UserItemRating> predicted = output.collect();
for(UserItemRating cell : predicted){
 System.out.println("predict(" + cell.getItem() + ", " + cell.getUser() + "): " + cell.getValue());
}

To find the correlation between any two users using their ratings on items:

The sample code below tries to find the correlation between users based on their ratings over items. Can be used to recommend items to a customer based on how similar his preferences are to other customers:

JavaSparkContext context = SparkContextFactory.createSparkContext("testing-1");

RatingTable ratingTable =new RatingTable();
ratingTable.addRating("Love at last", "Alice", 5);
ratingTable.addRating("Remance forever", "Alice", 5);
ratingTable.addRating("Nonstop car chases", "Alice", 0);
ratingTable.addRating("Sword vs. karate", "Alice", 0);
ratingTable.addRating("Love at last", "Bob", 5);
ratingTable.addRating("Cute puppies of love", "Bob", 4);
ratingTable.addRating("Nonstop car chases", "Bob", 0);
ratingTable.addRating("Sword vs. karate", "Bob", 0);
ratingTable.addRating("Love at last", "Carol", 0);
ratingTable.addRating("Cute puppies of love", "Carol", 0);
ratingTable.addRating("Nonstop car chases", "Carol", 5);
ratingTable.addRating("Sword vs. karate", "Carol", 5);
ratingTable.addRating("Love at last", "Dave", 0);
ratingTable.addRating("Remance forever", "Dave", 0);
ratingTable.addRating("Nonstop car chases", "Dave", 4);

JavaRDD<UserItemRating> input = context.parallelize(ratingTable.getRatings());

UserCorrelationRecommender recommender = new UserCorrelationRecommender();

JavaRDD<UserCorrelation> output = recommender.fitAndTransform(input);

List<UserCorrelation> predicted = output.collect();
for(UserCorrelation cell : predicted){
 System.out.println("user-correlation(" + cell.getUser1() + ", " + cell.getUser2() + "): " + cell.getPearson());
}

To find the correlation between any two movies using user rating:

The sample code below tries to find the correlation between items rated by users. Can be used for scenario such as "Customer who bought this also bought"

JavaSparkContext context = SparkContextFactory.createSparkContext("testing-1");

RatingTable ratingTable =new RatingTable();
ratingTable.addRating("Love at last", "Alice", 5);
ratingTable.addRating("Remance forever", "Alice", 5);
ratingTable.addRating("Nonstop car chases", "Alice", 0);
ratingTable.addRating("Sword vs. karate", "Alice", 0);
ratingTable.addRating("Love at last", "Bob", 5);
ratingTable.addRating("Cute puppies of love", "Bob", 4);
ratingTable.addRating("Nonstop car chases", "Bob", 0);
ratingTable.addRating("Sword vs. karate", "Bob", 0);
ratingTable.addRating("Love at last", "Carol", 0);
ratingTable.addRating("Cute puppies of love", "Carol", 0);
ratingTable.addRating("Nonstop car chases", "Carol", 5);
ratingTable.addRating("Sword vs. karate", "Carol", 5);
ratingTable.addRating("Love at last", "Dave", 0);
ratingTable.addRating("Remance forever", "Dave", 0);
ratingTable.addRating("Nonstop car chases", "Dave", 4);

JavaRDD<UserItemRating> input = context.parallelize(ratingTable.getRatings());

ItemCorrelationRecommender recommender = new ItemCorrelationRecommender();

JavaRDD<ItemCorrelation> output = recommender.fitAndTransform(input);

List<ItemCorrelation> predicted = output.collect();
for(ItemCorrelation cell : predicted){
 System.out.println("movie-correlation(" + cell.getItem1() + ", " + cell.getItem2() + "): " + cell.getPearson());
}

Recommend connection via mutual connections

The sample code below shows how to recommend connections between two individual who do not know each other but might have mutual connections:

List<Connection> connections = new ArrayList<>();
connections.add(new Connection("Alice", Arrays.asList("Bob", "Dave"))); // Alice knows Bob and Dave
connections.add(new Connection("Dave", Arrays.asList("Alice", "Carole")));
connections.add(new Connection("Bob", Arrays.asList("James", "Alice", "Jim")));
connections.add(new Connection("Jim", Arrays.asList("Bob", "Smith")));

JavaSparkContext context = SparkContextFactory.createSparkContext("testing-1");
JavaRDD<Connection> connectionJavaRDD = context.parallelize(connections);
ConnectionRecommender recommender = new ConnectionRecommender();
JavaRDD<ConnectionRecommendation> recommendationJavaRDD = recommender.fitAndTransform(connectionJavaRDD);

List<ConnectionRecommendation> recommendations = recommendationJavaRDD.collect();

for(ConnectionRecommendation recommendation : recommendations){
 System.out.println(recommendation.getPerson1() + " can be connected to " + recommendation.getPerson2() + " via " + recommendation.getCommonFriends());
}