Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
This work improves recommendation accuracy in multimedia systems by better modeling user behaviors, but it is incremental as it builds on existing non-sampling frameworks.
The paper tackles the problem of multi-behavior implicit recommendation by addressing limitations in existing non-sampling methods, such as ignoring varying user preference strengths and behavior dependencies, and proposes CHCF, which achieves state-of-the-art performance with improvements of up to 5.2% in NDCG@20 on real-world datasets.
Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework named Criterion-guided Heterogeneous Collaborative Filtering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.