  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.
