Research Article
RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context
@INPROCEEDINGS{10.1007/978-3-030-67537-0_39, author={Jun Zeng and Haoran Tang and Xin He}, title={RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Recommendation system Point of interest Region Context Collaborative filtering}, doi={10.1007/978-3-030-67537-0_39} }- Jun Zeng
Haoran Tang
Xin He
Year: 2021
RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_39
Abstract
In the past few years, mobile application has been innovated by leaps and bounds, which leads the prevalence of location-based social networks (LBSNs). Point of interest (POI) recommendation aims to recommend satisfactory locations to users in mobile environment and plays an important role in LBSNs. However, there are still two challenges to be solved. One is the data sparseness caused by users who just visit a few POIs. The other is that it’s hard to make reasonable explanation of recommendation from the perspective of real world. Hence, firstly we propose a region-based collaborative filtering to alleviate the data sparseness by clustering locations into regions. Secondly, we model the impact of two kinds of user contexts like geographical distance and POI category to make POI recommendation more reasonable. Finally, we present a joint model called RCFC which combines the two parts mentioned above. Results of experiments on two real-world datasets demonstrate the model we propose outperforms the popular recommendation algorithms and is more in line with the situation in real world.
