CF4J: Collaborative Filtering for Java

Abstract

Recommender Systems (RS) provide a relevant tool to mitigate the information overload problem. A large number of researchers have published hundreds of papers to improve different RS features. It is advisable to use RS frameworks that simplify RS researchers: a) to design and implement recommendations methods and, b) to speed up the execution time of the experiments. In this paper, we present CF4J, a Java library designed to carry out Collaborative Filtering based RS research experiments. CF4J has been designed from researchers to researchers. It allows: a) RS datasets reading, b) full and easy access to data and intermediate or final results, c) to extend their main functionalities, d) to concurrently execute the implemented methods, and e) to provide a thorough evaluation for the implementations by quality measures. In summary, CF4J serves as a library specifically designed for the research trial and error process.

Cited by in corpus (4)

• Providing reliability in Recommender Systems through Bernoulli Matrix Factorization
• Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities
• Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems
• Testing Deep Learning Recommender Systems Models on Synthetic GAN-Generated Datasets