Location Rating Based on Graph Neural Network
Authors: 
Fengbin Zheng, Li Ding, Lanxue DangAuthors Info & Claims
Pages 169 - 175
Abstract
With the rapid development of social networks, people are increasingly interested in sharing their location. Location recommendation has become an important personalization service for location-based social networks (LBSN). Location rating is one of the important tools. However, location-based social networks contain multidimensional network structures and node information. Existing approaches mostly focus on network structures that utilize one of these dimensions, which makes it difficult to efficiently aggregate information from multiple dimensions simultaneously. To overcome these difficulties, one of the recent approaches is the social recommendation based on graph neural networks (GNNs). Based on this, this paper proposes a graph neural network framework for location rating. Graph neural networks are highly inductive and can efficiently aggregate network structure and node information. In particular, the method not only aggregates homogeneous social networks composed of user and heterogeneous bipartite graphs composed of user-location, but also constructs location networks using location sequences to propose an aggregation model for location networks. Experiments are conducted on two datasets, and the results show that the method improves on the root mean square error (RMSE) and mean absolute error (MAE) metrics.
References
[1]
Bao J, Zheng Y, Wilkie D, et al. Recommendations in location-based social networks: a survey[J]. GeoInformatica, 2015, 19(3): 525--565.
[2]
Zhou J, Cui G, Zhang Z, et al. Graph neural networks: A review of methods and applications[J]. arXiv preprint arXiv:1812.08434, 2018.
[3]
Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016
[4]
Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint arXiv:1710.10903, 2017.
[5]
Fan W, Ma Y, Li Q, et al. Graph neural networks for social recommendation[C]//The World Wide Web Conference. 2019: 417--426.
[6]
Lian D, Zhao C, Xie X, et al. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014: 831--840.
[7]
Li X, Cong G, Li X L, et al. Rank-geofm: A ranking based geographical factorization method for point of interest recommendation[C]//Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 2015: 433--442.
[8]
Li H, Ge Y, Hong R, et al. Point-of-interest recommendations: Learning potential check-ins from friends[C]//Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016: 975--984.
[9]
Zhang J D, Chow C Y. iGSLR: personalized geo-social location recommendation: a kernel density estimation approach[C]//Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013: 334--343.
[10]
Tang J, Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 2018: 565--573.
[11]
Guo G, Zhang J, Yorke-Smith N. A novel recommendation model regularized with user trust and item ratings[J]. ieee transactions on knowledge and data engineering, 2016, 28(7): 1607--1620.
[12]
Senožetnik M, Bradeško L, Šubic T, et al. Estimating point-of-interest rating based on visitors geospatial behaviour[J]. Computer Science and Information Systems, 2019, 16(1): 131--154.
[13]
Zheng Y, Xie X, Ma W Y. GeoLife: A collaborative social networking service among user, location and trajectory[J]. IEEE Data Eng. Bull., 2010, 33(2): 32--39.
[14]
Su C, Jia X T, Xie X Z, et al. Community Detection and Location Recommendation Based on LBSN[C]//2017 International Conference on Network and Information Systems for Computers (ICNISC). IEEE, 2017: 227--231.
[15]
Wang H, Terrovitis M, Mamoulis N. Location recommendation in location-based social networks using user check-in data[C]//Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems. 2013: 374--383.
[16]
Bao J, Zheng Y, Mokbel M F. Location-based and preference-aware recommendation using sparse geo-social networking data[C]//Proceedings of the 20th international conference on advances in geographic information systems. 2012: 199--208.
[17]
Xu K, Hu W, Leskovec J, et al. How powerful are graph neural networks?[J]. arXiv preprint arXiv:1810.00826, 2018.
[18]
Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs[C]//Advances in neural information processing systems. 2017: 1024--1034. GraphSage
[19]
Ying R, He R, Chen K, et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018: 974--983.
[20]
Wang X, He X, Wang M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval. 2019: 165--174.
[21]
Liu Y, Pham T A N, Cong G, et al. An experimental evaluation of point-of-interest recommendation in location-based social networks[J]. 2017.
[22]
Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In 21th Conference on Neural Information Processing Systems, Vol. 1. 2--1.
[23]
Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and Knowledge Management. ACM, 931--940.
[24]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web, WWW. 173--182.
Index Terms
- Location Rating Based on Graph Neural Network
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Information
Published In
December 2020
597 pages
ISBN:9781450387828
DOI:10.1145/3444370
Copyright © 2020 ACM.
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In-Cooperation
- Sun Yat-Sen University
- CARLETON UNIVERSITY: INSTITUTE FOR INTERDISCIPLINARY STUDIES
- Beijing University of Posts and Telecommunications
- Guangdong University of Technology: Guangdong University of Technology
- Deakin University
Publisher
Association for Computing Machinery
New York, NY, United States
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Published: 04 January 2021
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CIAT 2020
CIAT 2020: 2020 International Conference on Cyberspace Innovation of Advanced Technologies
December 4 - 6, 2020
Guangzhou, China
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CIAT 2020 Paper Acceptance Rate 94 of 232 submissions, 41%;
Overall Acceptance Rate 94 of 232 submissions, 41%
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