KAUST DepartmentComputer Science
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
King Abdullah University of Science and Technology
Permanent link to this recordhttp://hdl.handle.net/10754/667564
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AbstractPoint-of-interest (POI) recommendation has become an increasingly important sub-field of recommendation system research. Previous methods employ various assumptions to exploit the contextual information for improving the recommendation accuracy. The common property among them is that similar users are more likely to visit similar POIs and similar POIs would like to be visited by the same user. However, none of existing methods utilize similarity explicitly to make recommendations. In this paper, we propose a new framework for POI recommendation, which explicitly utilizes similarity with contextual information. Specifically, we categorize the context information into two groups, i.e., global and local context, and develop different regularization terms to incorporate them for recommendation. A graph Laplacian regularization term is utilized to exploit the global context information. Moreover, we cluster users into different groups, and let the objective function constrain the users in the same group to have similar predicted POI ratings. An alternating optimization method is developed to optimize our model and get the final rating matrix. The results in our experiments show that our algorithm outperforms all the state-of-the-art methods.
CitationHan, P., Li, Z., Liu, Y., Zhao, P., Li, J., Wang, H., & Shang, S. (2020). Contextualized Point-of-Interest Recommendation. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. doi:10.24963/ijcai.2020/344
SponsorsThis work is supported by the National Natural Science Foundation of China (No. 61932004).
Conference/Event nameTwenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)