Enhancing Multi-Behavior Sequential Recommenders with Behavior-Aware Regularization

In the realm of multi-behavior sequential recommendation (MBSR), the complexity and heterogeneity of user interactions pose substantial challenges for sequence modeling. Existing studies involve significant efforts in combining different modules to learn more expressive multi-behavior sequence representations or designing strategies to extract user preferences related to the target behavior. Despite their effectiveness, these methods neglect a thorough analysis of how behavioral information shapes the probability distribution for next-item prediction, which is crucial for accurately modeling user preferences. To this end, we first analyze the learning distribution of MBSR, shedding light on the significance of target behavior in next-item prediction. Building upon this insight, we propose a Behavior-Aware Regularization approach for multi-behavior sequential Rec ommendation (BAR4Rec), where we introduce a regularization loss function to preserve the intrinsic constraints of target behavior. In this way, the target probability distribution is extracted from the whole distribution and naturally evolves into a more compatible and tractable form, thus facilitating model design and training. We evaluate the proposed method on three real-world datasets, and the results validate the efficacy of our approach.