SDBC-RO: STRONG-DEPENDENCY BEHAVIOR CHAIN GUIDED MULTI-VIEW BEHAVIOR SEMANTICS-AWARE REPRESENTATION OPTIMIZATION

With the continuous expansion of data scale and increasingly diverse interaction scenarios on e-commerce platforms, accurately capturing users’ true preferences and decision intentions from massive heterogeneous behavioral data has become a central challenge in Multi-Behavior Recommendation (MBR). Current mainstream MBR methods have an inherent flaw: their modeling relies heavily on predefined behavioral partial order relations, which largely deviate from users’ real behavior patterns. This easily causes over-strengthening of auxiliary behaviors and marginalization of target behaviors, ultimately leading to representation distortion. In addition, existing methods typically fail to fully leverage both users’ behavioral habits and category preferences, resulting in incomplete semantic representations. To address the above issues, we propose Strong-Dependency Behavior Chain Guided Multi-View Behavior Semantics-Aware Representation Optimization (SDBC-RO). We first define a strong-dependency behavior chain (SDBC) in the form of ”auxiliary behavior set set > target behavior”. This design avoids the cumulative interference from multi-level auxiliary behaviors while preserving the dominant role of the target behavior. Meanwhile, we jointly leverage large language models and knowledge graphs to model behavior semantics and behavioral partial order relations, so as to generate high-quality behavior representations. On this basis, we develop a two stage multi-view representation optimization framework guided by SDBC. In the former stage, a two-level heterogeneous hypergraph is constructed from user–item–category ternary interactions to learn the basic representations of users, items, and categories. In the latter, behavior semantics-aware knowledge graphs are constructed from user–user, item–item, and category–category relational views to deeply integrate behavior semantics with the three types of representations. Finally, adaptive cross chain fusion and category-aware representation enhancement are employed to improve recommendation performance. Extensive experiments on two real world benchmark datasets, Tmall and Taobao, demonstrate that SDBC-RO substantially outperforms state-of-the-art baselines, achieving relative improvements of 17.78% and 10.09% in HR@10 and NDCG@10 on Tmall, and 23.21% and 3.68% on Taobao, respectively