HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendation
This addresses the problem of improving recommendation accuracy for users with diverse interaction behaviors, though it appears incremental as it builds on existing attention and graph-based methods.
The paper tackles the challenge of capturing sequential patterns in multi-behavioral user interactions for recommendation systems by introducing HMAR, which uses hierarchical masked attention and historical behavior indicators, and it outperforms state-of-the-art methods on four real-world datasets.
In the context of recommendation systems, addressing multi-behavioral user interactions has become vital for understanding the evolving user behavior. Recent models utilize techniques like graph neural networks and attention mechanisms for modeling diverse behaviors, but capturing sequential patterns in historical interactions remains challenging. To tackle this, we introduce Hierarchical Masked Attention for multi-behavior recommendation (HMAR). Specifically, our approach applies masked self-attention to items of the same behavior, followed by self-attention across all behaviors. Additionally, we propose historical behavior indicators to encode the historical frequency of each items behavior in the input sequence. Furthermore, the HMAR model operates in a multi-task setting, allowing it to learn item behaviors and their associated ranking scores concurrently. Extensive experimental results on four real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods. Our code and datasets are available here (https://github.com/Shereen-Elsayed/HMAR).