The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Learning geographical preferences for point-of-interest recommendation
Abstract
The problem of point of interest (POI) recommendation is to provide personalized recommendations of places of interests, such as restaurants, for mobile users. Due to its complexity and its connection to location based social networks (LBSNs), the decision process of a user choose a POI is complex and can be influenced by various factors, such as user preferences, geographical influences, and user mobility behaviors. While there are some studies on POI recommendations, it lacks of integrated analysis of the joint effect of multiple factors. To this end, in this paper, we propose a novel geographical probabilistic factor analysis framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, the user mobility behaviors can be effectively exploited in the recommendation model. Moreover, the recommendation model can effectively make use of user check-in count data as implicity user feedback for modeling user preferences. Finally, experimental results on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art latent factor models with a significant margin.
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Cited by
Urban Computing: Concepts, Methodologies, and Applications
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,507
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623638 · Cited by 608
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
Zhao, P., Luo, A., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., & Zhou, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3007194 · Cited by 357
Exploiting Geographical Neighborhood Characteristics for Location Recommendation
Liu, Y., Wei, W., Sun, A., & Miao, C. (2014) · Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management · doi:10.1145/2661829.2662002 · Cited by 345
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
Li, X., Cong, G., Li, X.-L., Pham, T.-A. N., & Krishnaswamy, S. (2015) · Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/2766462.2767722 · Cited by 340
Metadata
- Authors
- Liu, B., Fu, Y., Yao, Z., & Xiong, H.
- Venue
- Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2013-08-11
- Pages
- 1043-1051
- OpenAlex
- W2009779426
- Cited by
- 437
- References
- 25
- FWCI
- 33.90
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, B., Fu, Y., Yao, Z., & Xiong, H. (2013). Learning geographical preferences for point-of-interest recommendation. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/2487575.2487673
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