Personalized recommendation with hybrid feedback by refining implicit data

In personalized recommender systems, the collaborative filtering (CF) recommendation approaches have been widely used to predict the preferences of users in real-world applications. Among them, Bayesian personalized ranking (BPR) attracts much attention as it can easily explore the binary form of implicit feedback. However, it still suffers from the absence problem of negative feedback. To address this issue, this paper proposes a hybrid-feedback collaborative filtering model by jointly exploiting the explicit and implicit feedback. Based on the assumption that users prefer items with high ratings, this work firstly introduces the definition of explicit rating data to the BPR model and further proposes an improved Bayesian personalized ranking (IBPR) model to jointly extract the implicit feedback features of users and items. The IBPR model alleviates the problem of lack of negative feedback and promotes the anti-noise performance of the recommender system. Then the IBPR and BiasSVD (Biased Singular Value Decomposition) models are combined to further extract explicit latent features of users as well as items and construct the hybrid-feedback CF model. In this model, the user–item ranking matrix is reconstructed based on the extracted implicit feedback features, and the rating matrix is constructed based on the extracted explicit feedback features. Our proposed method is evaluated on five public datasets and achieves the competitive performance.