Name
Title | ||
|---|---|---|
Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks. |
Abstract | ||
|---|---|---|
Point-of-Interest (POI) recommendation is one of the most essential tasks in LBSNs to help users discover new interesting locations, especially when users travel out of town or to unfamiliar areas. Current studies on POI recommendation in LBSNs mainly focus on modeling multiple factors extracted from users’ profiles and checking-in records. Data sparsity and incompleteness of user-POI interaction matrix are very common problems in POI recommendation, especially for the out-of-town scenario. Another challenge is that most information in the LBSNs is unreliable due to users’ different backgrounds or preferences. Because of the close relationship between users, information from trustable friends on Communication-Based Social Networks (CBSNs) is more valuable than that in LBSNs, which can give a preferable suggestion instead of trustless reviews in LBSNs. In this study, we propose a latent probabilistic generative model called HI-LDA (Heterogeneous Information based LDA), which can accurately capture users’ words on CBSNs by taking into full consideration the information on LBSNs including geographical effect as well as the abundant information including social relationship, users’ interactive behaviors and comment content. In particular, the parameters of the HI-LDA model can be inferred by the Gibbs sampling method in an effective fashion. Beyond these proposed techniques, we introduce an POI recommendation framework integrating geographical clustering approach considering the locations and popularity of POIs simultaneously. Extensive experiments were conducted to evaluate the performance of the proposed framework on two real heterogeneous LBSN-CBSN networks. The experimental results demonstrate the superiority of HI-LDA on effective and efficient POI recommendation in both home-town and out-of-town scenarios, when compared with the state-of-the-art baseline approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.09.060 | Neurocomputing |
Keywords | Field | DocType |
Location-based social networks,POI recommendation,Heterogeneous networks,Probabilistic graphical model | Social relationship,Social network,Popularity,Artificial intelligence,Probabilistic generative model,Point of interest,Cluster analysis,Gibbs sampling,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
373 | 0925-2312 | 1 |
PageRank | References | Authors |
0.36 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Xiong | 1 | 3 | 3.76 |
| Shaojie Qiao | 2 | 201 | 25.93 |
| Nan Han | 3 | 69 | 8.64 |
| Fei Xiong | 4 | 61 | 11.93 |
| Zhan Bu | 5 | 160 | 17.93 |
| Rong-Hua Li | 6 | 381 | 33.77 |
| Kun Yue | 7 | 2 | 0.71 |
| Guan Yuan | 8 | 57 | 5.42 |