Explicit and Implicit Modeling via Dual-Path Transformer for Behavior Set-informed Sequential Recommendation

Sequential recommendation (SR) and multi-behavior sequential recommendation (MBSR) both come from real-world scenarios. Compared with SR, MBSR takes into account the dependencies of different behaviors. We find that most existing works on MBSR are studied in the context of e-commerce scenarios. In terms of the data format of the behavior types, we observe that the conventional label-formatted data carries limited information and is inadequate for scenarios like social media. With this observation, we introducebehavior set and extend MBSR to behavior set-informed sequential recommendation (BSSR). In BSSR, behavior dependencies become more complex and personalized, and user interest arousal may lack explicit contextual associations. To delve into the dynamics inhered within a behavior set and adaptively tailor recommendation lists upon its variability, we propose a novel solution called Explicit and Implicit modeling via Dual-Path Transformer (EIDP) for BSSR. Our EIDP adopts a dual-path architecture, distinguishing between explicit modeling path (EMP) and implicit modeling path (IMP) based on whether to directly incorporate the behavior representations. EMP features the personalized behavior set-wise transition pattern extractor (PBS-TPE) as its core component. It couples behavioral representations with both the items and positions to explore intra-behavior dynamics within a behavior set at a fine granularity. IMP utilizes light multi-head self-attention blocks (L-MSAB) as encoders under specific behavior types. The obtained multi-view representations are then aggregated by cross-behavior attention fusion (CBAF), using the behavior set of the next time step as a guidance to extract collaborative semantics at the behavioral level. Extensive experiments on two real-world datasets demonstrate the effectiveness of our EIDP. We release the implementation code at: https://github.com/OshiNoCSMA/EIDP.