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Integrating spatial and temporal contexts into a factorization model for POI recommendation
Cai, Ling1,2; Xu, Jun1; Liu, Ju1,2; Pei, Tao1,2,3
2018
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Volume32Issue:3Pages:524-546
Corresponding AuthorXu, Jun(xujun@lreis.ac.cn)
AbstractMatrix 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.
KeywordCheck-in matrix factorization feature space separation POI recommendation
DOI10.1080/13658816.2017.1400550
WOS KeywordHUMAN MOBILITY PATTERNS ; SYSTEMS
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000422691100005
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/60529
Collection中国科学院地理科学与资源研究所
Corresponding AuthorXu, Jun
Affiliation1.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
Recommended Citation
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.
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