Hierarchical Intent-Based Interest Disentanglement for Personalized Recommendation

To address the data sparsity issue, conventional graph-based models leverage structural signals from the interaction graph to embed users’ interests. However, these models learn a uniform representation for interest modeling, which blends users’ diverse intents and inevitably biases interest learning, hindering recommendations. Although the fine-grained paradigm can learn the intents of interactions separately to alleviate learning bias, the relationships among intents and the disentangled manner require elaborate design. Existing fine-grained models emphasize intent diversity and employ additional data splitting for disentanglement, which ignores the hierarchical relationship, exacerbates data sparsity, and increases the computational burden. To address these issues, we explore hierarchical intents and adaptive intent learning, proposing a hierarchical intent-based interest disentanglement (HIID) model for personalized recommendation. HIID introduces learnable intent queries to guide interest disentanglement from global interactions in a split-free manner. It raises a hierarchical intent hypothesis to involve hierarchical CF signals for interest modeling, where intents within the same level appear relatively diverse, and the in-depth intents are abstracted from the superficial ones. Both adaptive intent learning and hierarchical hypothesis help extract significant CF signals to promote personalized recommendation. Extensive experiments on public datasets show that the proposed HIID outperforms the state-of-the-art CF models for recommendation. Furthermore, HIID implements adaptive interest disentanglement in a split-free manner, improving the training efficiency of the recommender model compared to the existing fine-grained interest models.