Understanding User Preferences in Location-Based Social Networks via a Novel Self-Attention Mechanism

Lei Shi, Jia Luo (), Peiying Zhang, Hongqi Han, Didier El Baz, Gang Cheng and Zeyu Liang
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Lei Shi: State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
Jia Luo: College of Economics and Management, Beijing University of Technology, Beijing 100021, China
Peiying Zhang: College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Hongqi Han: Institute of Scientific and Technical Information of China, Beijing 100038, China
Didier El Baz: LAAS-CNRS, Université de Toulouse, CNRS, 31031 Toulouse, France
Gang Cheng: School of Computer Science, North China Institute of Science and Technology, Beijing 065201, China
Zeyu Liang: School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China

Sustainability, 2022, vol. 14, issue 24, 1-14

Abstract: The check-in behaviors of users are ubiquitous in location-based social networks in urban living. Understanding user preferences is critical to improving the recommendation services of social platforms. In addition, great quality of recommendation is also beneficial to sustainable urban living since the user can easily find the point of interest (POI) to visit, which avoids unnecessary consumption, such as a longer time taken for searching or driving. To capture user preferences from their check-in behaviors, advanced methods transform historical records into graph structure data and further leverage graph deep learning-based techniques to learn user preferences. Despite their effectiveness, existing graph deep learning-based methods are limited to the capture of the deep graph’s structural information due to inherent limitations, such as the over-smoothing problem in graph neural networks, further leading to suboptimal performance. To address the above issues, we propose a novel method built on Transformer architecture named spatiotemporal aware transformer (STAT) via a novel graphically aware attention mechanism. In addition, a new temporally aware sampling strategy is developed to reduce the computational cost and enable STAT to deal with large graphs. Extensive experiments on real-world datasets have demonstrated the superiority of the STAT compared to state-of-the-art POI recommendation methods.

Keywords: user preference; social network; POI recommendation; deep learning; attention mechanism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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