Abstract
With the rapid development of location-based social networks (LBSNs), more and more people form the habit of sharing locations with their friends. Point of interest (POI) recommendation is aiming to recommend new places for users when they explore their surroundings. How to make proper recommendation has been a key point on the basis of existing information. In this paper, we propose a novel POI recommendation approach by fusing user preference, geographical influence and social reputation. TFIDF is used to represent user preference. Then, we further improve recommendation model by incorporating geographical distance and popularity. In the dataset, we find friends in LBSNs share low common visited POIs. Instead of directly getting recommendation from friends, users attain recommendation from others according to their reputation in the LBSNs. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.
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Acknowledgement
This research is supported by the National Natural Science Foundation of China (Grant No. 61502062, Grant No. 61672117 and Grant No. 61602070), the China Postdoctoral Science Foundation under Grant 2014M560704, the Scientific Research Foundation for the Returned Overseas Chinese Scholars (State Education Ministry), and the Fundamental Research Funds for the Central Universities Project No. 2015CDJXY.
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Zeng, J., Li, F., Wen, J., Zhou, W. (2018). A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_22
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DOI: https://doi.org/10.1007/978-3-030-00916-8_22
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