Time Lag Aware Sequential Recommendation

TL;DR

This paper introduces TLSRec, a novel sequential recommendation model that captures user preference dynamics by considering time lag and hierarchical preferences, improving recommendation accuracy.

Contribution

It proposes a hierarchical self-attention network combined with a neural time gate to better model and fuse long-term and short-term user preferences considering time lag effects.

Findings

TLSRec outperforms existing methods on real datasets.

The model effectively captures global and local preference shifts.

Time lag sensitive fusion improves recommendation relevance.

Abstract

Although a variety of methods have been proposed for sequential recommendation, it is still far from being well solved partly due to two challenges. First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference. Second, the existing methods often use a scalar based weighting schema to fuse the long-term and short-term preferences, which is too coarse to learn an expressive embedding of current preference. To address the two challenges, we propose a novel model called Time Lag aware Sequential Recommendation (TLSRec), which integrates a hierarchical modeling of user preference and a time lag sensitive fine-grained fusion of the long-term and short-term preferences. TLSRec employs a hierarchical self-attention network to learn users' preference at both global and local time scales, and a neural time gate to adaptively regulate the contributions of the long-term and short-term preferences for the learning of a user's current preference at the aspect level and based on the lag between the current time and the time of the last behavior of a user. The extensive experiments conducted on real datasets verify the effectiveness of TLSRec.