Personalized Long- and Short-term Preference Learning for Next POI Recommendation

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 4; pp. 1944 - 1957
• Main Authors: Wu, Yuxia, Li, Ke, Zhao, Guoshuai, Qian, Xueming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attention mechanism; Categories; Customization; Fuses; History; Learning; Markov processes; Modules; Next POI recommendation; personalization; Software engineering; Statistical analysis; user preference; Visualization
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.3002531

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model both users' general taste and recent sequential behaviors. Moreover, different users show different dependencies on the two parts. However, most existing methods learn the same dependencies for different users. Besides, the locations and categories of POIs contain different information about users' preference. However, current researchers always treat them as the same factors or believe that categories determine where to go. To this end, we propose a novel method named Personalized Long- and Short-term Preference Learning (PLSPL) to learn the specific preference for each user. Specially, we combine the long- and short-term preference via user-based linear combination unit to learn the personalized weights on different parts for different users. Besides, the context information such as the category and check-in time is also essential to capture users' preference. Therefore, in long-term module, we consider the contextual features of POIs in users' history records and leverage attention mechanism to capture users' preference. In the short-term module, to better learn the different influences of locations and categories of POIs, we train two LSTM models for location- and category-based sequence, respectively. Then we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.