Sequential Recommendation (SR) captures users' dynamic preferences by
modeling how users transit among items. However, SR models that utilize only
single type of behavior interaction data encounter performance degradation when
the sequences are short. To tackle this problem, we focus on Multi-Behavior
Sequential Recommendation (MBSR) in this paper, which aims to leverage
time-evolving heterogeneous behavioral dependencies for better exploring users'
potential intents on the target behavior. Solving MBSR is challenging. On the
one hand, users exhibit diverse multi-behavior patterns due to personal
characteristics. On the other hand, there exists comprehensive co-influence
between behavior correlations and item collaborations, the intensity of which
is deeply affected by temporal factors. To tackle these challenges, we propose
a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem,
which models personalized patterns and multifaceted sequential collaborations
in a novel way to boost recommendation performance. First, PBAT develops a
personalized behavior pattern generator in the representation layer, which
extracts dynamic and discriminative behavior patterns for sequential learning.
Second, PBAT reforms the self-attention layer with a behavior-aware
collaboration extractor, which introduces a fused behavior-aware attention
mechanism for incorporating both behavioral and temporal impacts into
collaborative transitions. We conduct experiments on three benchmark datasets
and the results demonstrate the effectiveness and interpretability of our
framework. Our implementation code is released at
this https URL

本文研究了 Multi-Behavior Sequential Recommendation 问题，提出了一种名为 Personalized Behavior-Aware Transformer (PBAT) 的框架，该框架通过建模个性化模式和多方面的序列协作来提高推荐性能。