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arXiv · 2312.09684

Context-Aware Sequential Model for Multi-Behaviour Recommendation

Shereen Elsayed·Ahmed Rashed·Lars Schmidt-Thieme
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Abstract

Sequential recommendation models are crucial for next-item recommendations in online platforms, capturing complex patterns in user interactions. However, many focus on a single behavior, overlooking valuable implicit interactions like clicks and favorites. Existing multi-behavioral models often fail to simultaneously capture sequential patterns. We propose CASM, a Context-Aware Sequential Model, leveraging sequential models to seamlessly handle multiple behaviors. CASM employs context-aware multi-head self-attention for heterogeneous historical interactions and a weighted binary cross-entropy loss for precise control over behavior contributions. Experimental results on four datasets demonstrate CASM's superiority over state-of-the-art approaches.

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Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme. 2023-12-15. Context-Aware Sequential Model for Multi-Behaviour Recommendation. https://arxiv.org/abs/2312.09684

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