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| Title | Mutual Graph Embedding In Lbsn: Capturing Multi-Factor Influence For Point-Of-Interest Prediction |
|---|---|
| ID_Doc | 38746 |
| Authors | Zhao Z.; Xu W.; Xue C.; Wang L. |
| Year | 2019 |
| Published | Proceedings - 2019 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Internet of People and Smart City Innovation, SmartWorld/UIC/ATC/SCALCOM/IOP/SCI 2019 |
| DOI | http://dx.doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00211 |
| Abstract | With the emerging of various location-based social networks (LBSNs), point-of-interest (POI) prediction becomes a hot research topic which helps to provide personalized location recommendation for users and help them discover new places and activities. Many factors affect user behaviors, such as geographical effect, temporal effect, and social effect. However, most of the previous work mainly focused on incorporating more factors, and lack consideration on combining multi-factor influence with an integrated model. Through investigating the specific graph structure in LBSNs, we propose the Mutual Graph Embedding in LBSNs (MGEL) model which innovatively captures the co-effect of multiple influential factors by incorporating them into POI and user attribute networks. Collaborative prediction is realized through embedding these factors into a shared low-dimensional semantic space. Under this scheme, MGEL is flexible to take new factors into consideration without changing the model structure. We conduct extensive experiments on large real-world datasets. Result shows that when considering the same factors, our method outperforms the state-of-the-art POI prediction models. © 2019 IEEE. |
| Author Keywords | Graph embedding; Multi-factor influence; POI prediction |
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