Research Article
A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks
@INPROCEEDINGS{10.1007/978-3-030-00916-8_22, author={Jun Zeng and Feng Li and Junhao Wen and Wei Zhou}, title={A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings}, proceedings_a={COLLABORATECOM}, year={2018}, month={10}, keywords={POI recommendation Collaborative filtering Location-based social networks Geographical influence}, doi={10.1007/978-3-030-00916-8_22} }- Jun Zeng
Feng Li
Junhao Wen
Wei Zhou
Year: 2018
A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-00916-8_22
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.