Java uses Mahout to recommend Collaborative filtering
Maven coordinates of dependent class libraries:
<dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-core</artifactId>
<version>0.13.0</version>
</dependency>
Mahout is an open source framework for machine learning and data mining, which includes many algorithms and tools for recommendation engines. Mahout's Collaborative filtering recommendation module is based on user behavior data, and predicts the possible interests and recommendations of users by analyzing their historical behavior and similarity with other users.
If there is a dependent dataset, you can store the dataset in a file. Each line represents the interaction information between a user and an item. The format can be user ID, item ID, and interaction intensity (such as rating). Here is an example dataset:
1,101,5
1,102,3
1,103,2
2,101,2
2,102,2
2,103,5
3,101,2
3,103,4
The following is a complete sample code recommended for Collaborative filtering using Mahout:
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.NearestNUserNeighborhood;
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.similarity.UserSimilarity;
import java.io.File;
import java.io.IOException;
import java.util.List;
public class CollaborativeFilteringExample {
public static void main(String[] args) {
try {
//Load Dataset
DataModel dataModel = new FileDataModel(new File("data.csv"));
//Building similarity measures
UserSimilarity similarity = new PearsonCorrelationSimilarity(dataModel);
//Building user neighbors
UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, similarity, dataModel);
//Build recommender
GenericUserBasedRecommender recommender = new GenericUserBasedRecommender(dataModel, neighborhood, similarity);
//Generate Top N recommendations for user ID 1
List<RecommendedItem> recommendations = recommender.recommend(1, 3);
//Print recommendation results
for (RecommendedItem recommendation : recommendations) {
System.out.println(recommendation);
}
}Catch (IOException | TasteException e){
e.printStackTrace();
}
}
}
This example first loads the dataset and then constructs a user similarity measure based on Pearson correlation coefficient. Next, a user filter was initialized based on neighbor size, similarity measurement, and data model. Finally, use the recommended method to generate Top N recommended items for a given user and print them out.
Summary:
Mahout is a powerful recommendation engine framework. It is very convenient to use Mahout for Collaborative filtering recommendation in Java. We can easily implement Collaborative filtering recommendation algorithms by loading datasets, building similarity measures, building user neighbors, and building recommenders. The above sample code provides a basic example to demonstrate how to use Mahout for recommendations. Using Mahout's Collaborative filtering recommendation module can help us achieve an efficient personalized recommendation system.
