Papers › Multi-Behavioral Sequential Recommendation

Multi-Behavioral Sequential Recommendation

8 Oct 2024RecSys 2024 10archive 2025-07-28

Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme

Sequential recommendation models are crucial for next-item prediction tasks in various online platforms, yet many focus on a single behavior, neglecting valuable implicit interactions. While multi-behavioral models address this using graph-based approaches, they often fail to capture sequential patterns simultaneously. Our proposed Multi-Behavioral Sequential Recommendation framework (MBSRec) captures the multi-behavior dependencies between the heterogeneous historical interactions via multi-head self-attention. Furthermore, we utilize a weighted binary cross-entropy loss for precise behavior control. Experimental results on four datasets demonstrate MBSRec’s significant outperformance of state-of-the-art approaches. The implementation code is available here https://github.com/Shereen-Elsayed/MBSRec.

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