An efficient incremental collaborative filtering system

Abstract

Collaborative filtering (CF) systems aim at recommending a set of personalized items for an active user, according to the preferences of other similar users. Many methods have been developed and some, such those based on Similarity and Matrix Factorization (MF) can achieve very good recommendation accuracy, but unfortunately they are computationally prohibitive. Thus, applying such approaches to realworld applications in which available information evolves frequently, is a non-trivial task. To address this problem, we propose a novel efficient incremental CF system, based on a weighted clustering approach. Our system is able to provide a high quality of recommendations with a very low computation cost. Experimental results on several real-world datasets, confirm the efficiency and the effectiveness of our method by demonstrating that it is significantly better than existing incremental CF methods in terms of both scalability and recommendation quality.