Context-Aware Sequential Model for Multi-Behaviour Recommendation

Code

• github.com/shereen-elsayed/casm
  OfficialIn papertf★ 10
• github.com/ariaattar/CASM-PyTorch
  pytorch★ 10
• github.com/Shereen-Elsayed/MBSRec
  tf★ 10

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.

Tasks

Reproductions