Integrating spatial and temporal contexts into a factorization model for POI recommendation
文献类型：期刊论文
| 作者 | Cai, Ling1,2; Xu, Jun1 ; Liu, Ju1,2; Pei, Tao1,2,3
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| 刊名 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
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| 出版日期 | 2018 |
| 卷号 | 32期号:3页码:524-546 |
| 关键词 | Check-in matrix factorization feature space separation POI recommendation |
| ISSN号 | 1365-8816 |
| DOI | 10.1080/13658816.2017.1400550 |
| 通讯作者 | Xu, Jun(xujun@lreis.ac.cn) |
| 英文摘要 | Matrix factorization is one of the most popular methods in recommendation systems. However, it faces two challenges related to the check-in data in point of interest (POI) recommendation: data scarcity and implicit feedback. To solve these problems, we propose a Feature-Space Separated Factorization Model (FSS-FM) in this paper. The model represents the POI feature spaces as separate slices, each of which represents a type of feature. Thus, spatial and temporal information and other contexts can be easily added to compensate for scarce data. Moreover, two commonly used objective functions for the factorization model, the weighted least squares and pairwise ranking functions, are combined to construct a hybrid optimization function. Extensive experiments are conducted on two real-life data sets: Gowalla and Foursquare, and the results are compared with those of baseline methods to evaluate the model. The results suggest that the FSS-FM performs better than state-of-the-art methods in terms of precision and recall on both data sets. The model with separate feature spaces can improve the performance of recommendation. The inclusion of spatial and temporal contexts further leverages the performance, and the spatial context is more influential than the temporal context. In addition, the capacity of hybrid optimization in improving POI recommendation is demonstrated. |
| WOS关键词 | HUMAN MOBILITY PATTERNS ; SYSTEMS |
| 资助项目 | National Key Research and Development Program[2017YFB0503604] ; NSFC Innovation Research Group Project[41421001] ; NSFC General Program[41371380] ; NSFC General Program[41771477] ; Innovation Project of LREIS[O88RA20BYA] ; Key Programs of the Chinese Academy of Sciences[QYZDY-SSW-DQC007] |
| WOS研究方向 | Computer Science ; Geography ; Physical Geography ; Information Science & Library Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000422691100005 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 资助机构 | National Key Research and Development Program ; NSFC Innovation Research Group Project ; NSFC General Program ; Innovation Project of LREIS ; Key Programs of the Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/60528]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Xu, Jun |
| 作者单位 | 1.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Ling,Xu, Jun,Liu, Ju,et al. Integrating spatial and temporal contexts into a factorization model for POI recommendation[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2018,32(3):524-546. |
| APA | Cai, Ling,Xu, Jun,Liu, Ju,&Pei, Tao.(2018).Integrating spatial and temporal contexts into a factorization model for POI recommendation.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,32(3),524-546. |
| MLA | Cai, Ling,et al."Integrating spatial and temporal contexts into a factorization model for POI recommendation".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 32.3(2018):524-546. |
入库方式： OAI收割
来源：地理科学与资源研究所
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