12 classical researchs on POI recommendation are gathered with coresponding paper and code.
ref: Yiding Liu, Tuan-Anh Pham, Gao Cong, Quan Yuan:
An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks. 1010 - 1021. http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/paper.pdf
1.Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation
PDF: http://www.cse.cuhk.edu.hk/irwin.king.new/_media/presentations/p325.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/USG.zip
data: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/data/Gowalla.zip
2.Fused Matrix Factorization with Geographical and Social Influence in Location-based Social Networks
PDF: http://www.cse.cuhk.edu.hk/lyu/_media/conference/cheng-aaai12.pdf?id=home&cache=cache
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/MGMPFM.zip
data: is the same as 1.
Points of my interest:
paper: Multi-center check-in behaviour
codes: sparse matrix computation using scipy
more details about csipy.sparce please refer to https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse
3.Exploring temporal effects for location recommendation on location-based social networks
PDF: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.416.4425&rep=rep1&type=pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/LRT.zip
data: is the same as 1.
4.Personalized geo-social location recommendation a kernel density estimation approach
PDF: http://www.cs.cityu.edu.hk/~chiychow/papers/ACMGIS_2013a.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/iGSLR.zip
data: is the same as 1.
Points of my interest:
paper: a unified framework integrating user preference, social influence, the geographical influence of users, and the personalized geographical influence of locations
codes: a smart way of computing 'sum' with return value
10.Exploiting geographical, social and categorical correlations for point-of-interest recommendations
PDF: http://www.cs.cityu.edu.hk/~chiychow/papers/SIGIR_2015.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoSoCa.zip
data: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/data/Yelp.zip
Points of my interest:
paper: variable-bandwidth KDE
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Long time no sign in (so long as to forgot password:) ). Besides continue updating 12 papers, I am going to follow up with state-of-art works on recommendation systems, which will be presented in the new archive named "RecSys 2018-" More detailed discussion is also documented there.
2019.3.12
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5.Location recommendation in location-based social networks using user check-in data
PDF: https://www.researchgate.net/profile/Manolis_Terrovitis/publication/260294299_Location_Recommendation_in_Location-based_Social_Networks_using_User_Check-in_Data/links/0f317530ac8b50c7bc000000.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/LFBCA.zip
data: is the same as 1.
6.Lore: Exploiting sequential influence for location recommendations
PDF: http://users.wpi.edu/~yli15/Includes/SIGSPATIAL2014_lore.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/LORE.zip
data: is the same as 1.
- Exploiting geographical neighborhood characteristics for location recommendation
PDF:http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4772&context=sis_research
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/IRenMF.zip
data: is the same as 1.
8.Geomf: Joint geographical modeling and matrix factorization for point-of-interest recommendation
PDF: http://staff.ustc.edu.cn/~cheneh/paper_pdf/2014/Defu-Lian-KDD.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoMF.zip
data: is the same as 1.
*9.Rank-geofm: A ranking based geographical factorization method for point of interest recommendation
PDF: https://www.researchgate.net/profile/Xutao_Li2/publication/278031194_Rank-GeoFM_A_Ranking_based_Geographical_Factorization_Method_for_Point_of_Interest_Recommendation/links/55d2c94e08ae0b8f3ef8e812.pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/RankGeoFM.zip
data: is the same as 1.
Points of my interest: The novelty of Rank-GeoFM lies in its definition of loss function. It considers the incompatibility between rankings and estimated scores of POIs. It worth mentioned that among all 12 models, Rank-GeoFM performs almost always the best in various metrics, according to Liu et al..
11.Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations
PDF: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.703.9373&rep=rep1&type=pdf
code: http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/code/GeoSoCa.zip
data: is the same as 1.
12.Point-of-interest recommendations: Learning potential check-ins from friends
PDF: https://www.kdd.org/kdd2016/papers/files/rfp0448-liA.pdf
code: comming soon
data: is the same as 1.