Incremental Collaborative Filtering recommender based on Regularized Matrix Factorization

Abstract

The Matrix-Factorization (MF) based models have become popular when building Collaborative Filtering (CF) recommenders, due to the high accuracy and scalability. However, most of the current MF based models are batch models that are incapable of being incrementally updated; while in real world applications users always enjoy receiving quick responses from the system once they have made feedbacks. In this work, we aim to design an incremental CF recommender based on the Regularized Matrix Factorization (RMF). To achieve this objective, we first simplify the training rule of RMF to propose the SI-RMF, which provides a simple mathematic form for further investigation; whereby we design two Incremental RMF models, respectively are the Incremental RMF (IRMF) and the Incremental RMF with linear biases (IRMF-B). The experiments on two large, real datasets suggest positive results, which prove the efficiency of our strategy.