  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.
