Abstract
Point-of-interest (POI) recommendation is a challenging task since check-in data is extremely sparse and the social relationships in traditional recommendation have a limited effect. To solve this challenge, we propose a new geographical model with social influence and user preference. More specifically, we firstly propose a business circle conception which is more suitable for the modern consumption pattern in an urban city in POI recommendation. Then we decompose the user-location matrix into two geographical latent factors and integrate them into our business circle framework. Besides, we incorporate the user preference as a regularization of matrix factorization framework into our model by means of aggregating overlapping interest communities of users via their check-ins categories. Extensive experiments are conducted on two real-world datasets and the experimental results demonstrate that our model outperforms other existing algorithms.
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Acknowledgments
This work was supported by NSFC (61502543), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2016TQ03X542), and the Fundamental Research Funds for the Central Universities (16lgzd15).
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Li, MR., Huang, L., Wang, CD. (2017). Geographical and Overlapping Community Modeling Based on Business Circles for POI Recommendation. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_60
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DOI: https://doi.org/10.1007/978-3-319-67777-4_60
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