Pairwise Preference over Multi-type Implicit Feedback Confidence Based Bayesian Personalized Ranking for Collaborative Filtering

Implicit feedback (e.g., purchases, views, clicks) have been receiving more attention due to their close relationship with real applications, including shopping, making friends, healthcare, etc. In some recent works, pairwise ranking algorithms have been shown to leverage different types of users' behaviors to solve the recommendation problem of heterogeneous implicit feedback. The ever-growing healthcare websites impose a new challenge for medical services recommendation. In this paper, we develop a novel preference learning algorithm to learn a confidence over multiple users' actions, which is called Multi-type Implicit Feedback Confidence Learned Bayesian Personalized Ranking (MTC-BPR). MTC-BPR has the merits of generating positive data from multiple auxiliary feedback and accommodates both the original and generated data for pairwise ranking. Experimental results on the real-world dataset from a healthcare website demonstrate that MTC-BPR achieves more accurate recommendations than several state-of-the-art methods.