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Article type: Research Article
Authors: You, Yanlina; * | Wang, Zhenyub
Affiliations: [a] Network and Information Center, Liaodong University, Dandong, China | [b] Network Center, Shandong Vocational and Technical University of International Studies, RiZhao, China
Correspondence: [*] Corresponding author. Yanlin You, Network and Information Center, Liaodong University, Dandong, China. E-mail: youyanlin@elnu.edu.cn.
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.
Keywords: Point-of-interest recommendation, user preference, Bayesian Personalized Ranking, embedding
DOI: 10.3233/JIFS-222705
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7113-7119, 2023
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