STAR: A Session-Based Time-Aware Recommender System
This work addresses the limitation of ignoring temporal details in session-based recommenders, offering an incremental improvement for anonymous user recommendation systems.
The paper tackles the problem of session-based recommendation by incorporating temporal intervals between events to better capture momentary user interests, achieving improved performance over state-of-the-art models on Yoochoose and Diginetica datasets in Recall and MRR metrics.
Session-Based Recommenders (SBRs) aim to predict users' next preferences regard to their previous interactions in sessions while there is no historical information about them. Modern SBRs utilize deep neural networks to map users' current interest(s) during an ongoing session to a latent space so that their next preference can be predicted. Although state-of-art SBR models achieve satisfactory results, most focus on studying the sequence of events inside sessions while ignoring temporal details of those events. In this paper, we examine the potential of session temporal information in enhancing the performance of SBRs, conceivably by reflecting the momentary interests of anonymous users or their mindset shifts during sessions. We propose the STAR framework, which utilizes the time intervals between events within sessions to construct more informative representations for items and sessions. Our mechanism revises session representation by embedding time intervals without employing discretization. Empirical results on Yoochoose and Diginetica datasets show that the suggested method outperforms the state-of-the-art baseline models in Recall and MRR criteria.