An incremental approach for collaborative filtering in streaming scenarios

Abstract

The crux of a recommendation engine is to process users ratings and provide personalized suggestions to the user. However, processing the ratings and providing recommendations in real time still remains challenging, when there is a perpetual influx of new ratings. Traditional approaches fail to accommodate the new streamlined ratings and update the users’ preferences on the fly. In this paper, we address this challenge of streaming data without compromising accuracy and efficiency of recommender system. We identify the affected users and incrementally update their vital statistics after each new rating. We propose an incremental similarity measure for finding neighbors who play an important role in personalizing recommendations for active user. Experimental results on real-world datasets show that the proposed approach outperforms the state-of-the-art techniques in terms of accuracy and execution time.