  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.
