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.

翻译：序列推荐模型对于在线平台中的下一项推荐至关重要，能够捕捉用户交互中的复杂模式。然而，许多模型仅关注单一行为，忽略了点击、收藏等有价值的隐式交互。现有的多行为模型往往无法同时捕捉序列模式。我们提出CASM——一种上下文感知序列模型，利用序列模型无缝处理多种行为。CASM采用上下文感知的多头自注意力机制处理异构历史交互，并通过加权二元交叉熵损失函数精确控制行为贡献。在四个数据集上的实验结果表明，CASM优于现有最先进方法。