Abstract
With the rapid development of mobile Internet, smart devices, and positioning technologies, location-based social networks (LBSNs) are growing rapidly. In LBSNs, point-of-interest (POI) recommendation is a crucial personalized location service that has become a research hotspot. To address extreme sparsity of user check-in data, a growing line of research exploits spatial-temporal information, social relationship, content information, popularity, and other factors to improve recommendation performance. However, the temporal and spatial transfers of user preferences are seldom mentioned in existing works, and interpretability, which is an important factor to enhance credibility of recommendation result, is overlooked. To cope with these issues, we propose a context-aware POI recommendation framework, which integrates users’ long-term static and time-varying preferences to improve recommendation performance and provide explanations. Experimental results over two real-world LBSN datasets demonstrate that the proposed solution has better performance than other advanced POI recommendation approaches.
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Acknowledgment
This work is supported in part by the National Science Foundation of China under Grant No. 61672276, the National Key Research and Development Program of China under Grant No. 2017YFB1400600, Jiangsu Natural Science Foundation of China under Grant No. BK20171037, and the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, G., Qi, L., Zhang, X., Xu, X., Dou, W. (2019). Context-Aware Point-of-Interest Recommendation Algorithm with Interpretability. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_50
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DOI: https://doi.org/10.1007/978-3-030-30146-0_50
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