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.
@article{arxiv.2312.09684,
title = {Context-Aware Sequential Model for Multi-Behaviour Recommendation},
author = {Shereen Elsayed and Ahmed Rashed and Lars Schmidt-Thieme},
journal= {arXiv preprint arXiv:2312.09684},
year = {2023}
}