Time-Aware Similarity Integration for User-Based Collaborative Filtering

Abstract

A time-aware collaborative filtering-based recommender system provides item recommendations for the current user by prioritizing recent items preferred by similar neighbors over past preferred items. The similarity measure critically affects the performance of the system, and this study focuses on measuring the similarity between users that changes over time. After dividing the users’ rating time into intervals and computing similarity for each time interval, the final similarity is generated as a weighted sum by assigning lower weights to past similarity values and higher weights to more recent similarity values. Additionally, to ensure continuity of similarity measurement, consecutive time intervals are set to overlap. As a result of experiments applying the proposed method to the existing similarity measures, significant performance improvement was achieved in terms of some of the major performance metrics. In particular, the degree of coverage improvement was the highest, and the performance improvement effect was higher when the overlap size between time intervals was large rather than when it was small.