Preference-aware Bayesian Personalized Ranking for Point-of-interest recommendation

Abstract

Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods.