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With the increasing scale and diversification of interaction behaviors in E-commerce, more and more researchers pay attention to multi-behavior recommender systems that utilize interaction data of other auxiliary behaviors such as view and cart. To address these challenges in heterogeneous scenarios, 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 underline{C}riterion-guided underline{H}eterogeneous underline{C}ollaborative underline{F}iltering (CHCF). CHCF introduces both upper and lower bounds 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 on target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by CHCF learning framework in a non-sampling form effectively. Extensive experiments on two real-world datasets show that CHCF outperforms the state-of-the-art methods in heterogeneous scenarios.
This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first convert a users implicit feedback into a like vector and a confidence vecto
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveragin
The item cold-start problem seriously limits the recommendation performance of Collaborative Filtering (CF) methods when new items have either none or very little interactions. To solve this issue, many modern Internet applications propose to predict
With increasing and extensive use of electronic health records, clinicians are often under time pressure when they need to retrieve important information efficiently among large amounts of patients health records in clinics. While a search function c
Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between