An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering

Collaborative filtering (CF)-based recommenders are achieved by matrix factorization (MF) to obtain high prediction accuracy and scalability. Most current MF-based models, however, are static ones that cannot adapt to incremental user feedbacks. This work aims to develop a general, incremental- and-static-combined scheme for MF-based CF to obtain highly accurate and computationally affordable incremental recommenders. With it, a recommender is designed to consist of two components, i.e., a static one built on static rating data, and an incremental one built on a sub-matrix related to rating-variations only. Highly reliable predictions are thus generated by fusing their results. The experiments on large industrial datasets show that desired accuracy and acceptable computational complexity are achieved by the resulting recommender with the proposed scheme.

I. Introduction

Web data explode with the rapid progress of the World-Wide-Web. It becomes common for online-recruiters to receive thousands of resumes, for online-consumers to see numerous commodities, and for web-surfers to face millions of pages. In a word, this information explosion has caused a serious problem: how to filter the truly desired information out of billions of bytes?