skip to main content
10.1145/3428658.3430970acmconferencesArticle/Chapter ViewAbstractPublication PageswebmediaConference Proceedingsconference-collections
research-article

A Survey on Point-of-Interest Recommendation in Location-based Social Networks

Published: 30 November 2020 Publication History

Abstract

The popularization of Location-based social networks (LBSNs) in last years has provided a lot of improvements in several Recommender Systems to the task of points-of-interest (POI) recommendation. In this paper, we provide an updated view of the POI recommendation, identifying relevant efforts, results, contributions, and limitations. Through a systematic mapping, we selected 73 relevant papers published in the last three years (2017, 2018, and 2019) in the main vehicles of the area (e.g., RecSys, VLDB, SIGIR, WWW, TKDE, etc.). As major limitations, first, we identified that these works prioritize accuracy over other quality dimensions, despite the consensus in the RS community that accuracy is not enough to assess the practical effectiveness of RSs. Further, we found a low intersection of metrics and datasets used in these works, along with a large number of metrics used in a few distinct studies. These observations show restrictions for reproducibility and straightforward comparison of results in the area. Finally, we highlight as a promising future work the in-depth exploitation of textual data, since just a few of the evaluated papers marginally use this rich data source.

References

[1]
Mohammad Aliannejadi and Fabio Crestani. 2018. Personalized Context-Aware Point of Interest Recommendation. ACM Transactions on Information Systems 36, 4 (2018), 1--28.
[2]
Mohammad Aliannejadi, Dimitrios Rafailidis, and Fabio Crestani. 2019. A Joint Two-Phase Time-Sensitive Regularized Collaborative Ranking Model for Point of Interest Recommendation. IEEE Transactions on Knowledge and Data Engineering nil, nil (2019), 1--1.
[3]
Aris Anagnostopoulos, Reem Atassi, Luca Becchetti, Adriano Fazzone, and Fabrizio Silvestri. 2016. Tour Recommendation for Groups. Data Mining and Knowledge Discovery 31, 5 (2016), 1157--1188.
[4]
Ramesh Baral, SS Iyengar, Xiaolong Zhu, Tao Li, and Pawel Sniatala. 2019. HiRecS: A Hierarchical Contextual Location Recommendation System. IEEE Transactions on Computational Social Systems 6, 5 (2019), 1020--1037.
[5]
Ramesh Baral, S. S. Iyengar, Tao Li, and N. Balakrishnan. 2018. CLoSe. In Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18.
[6]
Tom Bewley and Iván Palomares Carrascosa. 2019. On Tour: Harnessing Social Tourism Data for City and Point of Interest Recommendation. Proceedings DSRS-Turing'19. London, 21-22nd Nov, 2019 (2019).
[7]
Chenzhong Bin, Tianlong Gu, Yanpeng Sun, and Liang Chang. 2019. A personalized POI route recommendation system based on heterogeneous tourism data and sequential pattern mining. Multimedia Tools and Applications 78, 24 (2019), 35135--35156.
[8]
Ling Cai, Jun Xu, Ju Liu, and Tao Pei. 2017. Integrating Spatial and Temporal Contexts Into a Factorization Model for Poi Recommendation. International Journal of Geographical Information Science 32, 3 (2017), 524--546.
[9]
Rodrigo Carvalho, Nícollas Silva, Luiz Chaves, Adriano CM Pereira, and Leonardo Rocha. 2019. Geographic-categorical diversification in POI recommendations. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web. 349--356.
[10]
Buru Chang, Yonggyu Park, Donghyeon Park, Seongsoon Kim, and Jaewoo Kang. 2018. Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation. In Proceedings of the Twenty-Seventh Intl. Joint Conference on Artificial Intelligence.
[11]
Jing Chen and Wenjun Jiang. 2019. Context-Aware Personalized POI Sequence Recommendation. In International Conference on Smart City and Informatization. Springer, 197--210.
[12]
Jinpeng Chen, Wen Zhang, Pei Zhang, Pinguang Ying, Kun Niu, and Ming Zou. 2018. Exploiting Spatial and Temporal for Point of Interest Recommendation. Complexity 2018, nil (2018), 1--16.
[13]
Madhuri Debnath, Praveen Kumar Tripathi, Ashis Kumer Biswas, and Ramez Elmasri. 2018. Preference Aware Travel Route Recommendation with Temporal Influence. In Proceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks - LocalRec'18.
[14]
Ruifeng Ding and Zhenzhong Chen. 2018. Recnet: a Deep Neural Network for Personalized Poi Recommendation in Location-Based Social Networks. International Journal of Geographical Information Science 32, 8 (2018), 1631--1648.
[15]
Ruifeng Ding, Zhenzhong Chen, and Xiaolei Li. 2018. Spatial-Temporal Distance Metric Embedding for Time-Specific Poi Recommendation. IEEE Access 6, nil (2018), 67035--67045.
[16]
Khoa D. Doan, Guolei Yang, and Chandan K. Reddy. 2019. An Attentive Spatio-Temporal Neural Model for Successive Point of Interest Recommendation. Springer International Publishing, 346--358.
[17]
Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee. 2017. Poi2vec: Geographical latent representation for predicting future visitors. In Thirty-First AAAI Conference on Artificial Intelligence.
[18]
Daniel Fleder and Kartik Hosanagar. 2009. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management science 55, 5 (2009), 697--712.
[19]
Rong Gao, Jing Li, Xuefei Li, Chengfang Song, and Yifei Zhou. 2018. A Personalized Point-Of-Interest Recommendation Model Via Fusion of Geo-Social Information. Neurocomputing 273, nil (2018), 159--170.
[20]
Lei Guo, Haoran Jiang, and Xinhua Wang. 2018. Location Regularization-Based Poi Recommendation in Location-Based Social Networks. Information 9, 4 (2018), 85.
[21]
Lei Guo, Yufei Wen, and Fangai Liu. 2019. Location perspective-based neighborhood-aware POI recommendation in location-based social networks. Soft Computing 23, 22 (2019), 11935--11945.
[22]
Qing Guo, Zhu Sun, Jie Zhang, Qi Chen, and Yin-Leng Theng. 2017. Aspect-aware Point-of-Interest Recommendation with Geo-Social Influence. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17.
[23]
Jungkyu Han and Hayato Yamana. 2017. Geographical Diversification in POI Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17.
[24]
Jing He, Xin Li, and Lejian Liao. 2018. Next Point-Of-Interest Recommendation Via a Category-Aware Listwise Bayesian Personalized Ranking. Journal of Computational Science 28, nil (2018), 206--216.
[25]
Jing He, Xin Li, Lejian Liao, and Williamb K. Cheung. 2018. Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference. CoRR abs/1805.06316 (2018). arXiv:1805.06316 http://arxiv.org/abs/1805.06316
[26]
Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 5--53.
[27]
Saeid Hosseini, Hongzhi Yin, Xiaofang Zhou, Shazia Sadiq, Mohammad Reza Kangavari, and Ngai-Man Cheung. 2018. Leveraging Multi-Aspect Time-Related Influence in Location Recommendation. World Wide Web 22, 3 (2018), 1001--1028.
[28]
Jianfeng Huang, Yuefeng Liu, Yue Chen, and Chen Jia. 2019. Dynamic Recommendation of POI Sequence Responding to Historical Trajectory. ISPRS International Journal of Geo-Information 8, 10 (2019), 433.
[29]
Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. 2019. An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation. IEEE Transactions on Services Computing PP (05 2019), 1--1.
[30]
Liwei Huang, Yutao Ma, Shibo Wang, and Yanbo Liu. 2019. An Attention-Based Spatiotemporal Lstm Network for Next Poi Recommendation. IEEE Transactions on Services Computing nil, nil (2019), 1--1.
[31]
Xu Jiao, Yingyuan Xiao, Wenguang Zheng, Hongya Wang, and Youzhi Jin. 2019. R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest. In The World Wide Web Conference on - WWW '19.
[32]
Kyunghan Lee, Seongik Hong, Seong Joon Kim, Injong Rhee, and Song Chong. 2009. Slaw: A new mobility model for human walks. In IEEE INFOCOM 2009. IEEE, 855--863.
[33]
Ranzhen Li, Yanyan Shen, and Yanmin Zhu. 2018. Next Point-of-Interest Recommendation with Temporal and Multi-level Context Attention. In 2018 IEEE International Conference on Data Mining (ICDM).
[34]
Hongwei Liang and Ke Wang. 2018. Top-k Route Search through Submodularity Modeling of Recurrent POI Features. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18.
[35]
Guoqiong Liao, Shan Jiang, Zhiheng Zhou, Changxuan Wan, and Xiping Liu. 2018. POI Recommendation of Location-Based Social Networks Using Tensor Factorization. In 2018 19th IEEE International Conference on Mobile Data Management (MDM).
[36]
Jinzhi Liao, Jiuyang Tang, Xiang Zhao, and Haichuan Shang. 2018. Improving Poi Recommendation Via Dynamic Tensor Completion. Scientific Programming 2018, nil (2018), 1--11.
[37]
Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2017. Personalized Trip Recommendation for Tourists Based on User Interests, Points of Interest Visit Durations and Visit Recency. Knowledge and Information Systems 54, 2 (2017), 375--406.
[38]
Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. 2013. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1043--1051.
[39]
Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next Location: A Recurrent Model with Spatial and Temporal Contexts. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI'16). 194--200.
[40]
Shudong Liu. 2017. User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art. CoRR abs/1712.06768 (2017). arXiv:1712.06768 http://arxiv.org/abs/1712.06768
[41]
Shudong Liu and Lei Wang. 2018. A Self-Adaptive Point-Of-Interest Recommendation Algorithm Based on a Multi-Order Markov Model. Future Generation Computer Systems 89, nil (2018), 506--514.
[42]
Wei Liu, Hanjiang Lai, Jing Wang, Geyang Ke, Weiwei Yang, and Jian Yin. 2019. Mix Geographical Information Into Local Collaborative Ranking for Poi Recommendation. World Wide Web nil, nil (2019).
[43]
Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, and Quan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. (2017).
[44]
R. Logesh and V. Subramaniyaswamy and. 2017. A Reliable Point of Interest Recommendation Based on Trust Relevancy Between Users. Wireless Personal Communications 97, 2 (2017), 2751--2780.
[45]
R Logesh and V Subramaniyaswamy. 2017. Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation. Journal of Information Science & Engineering 33, 6 (2017).
[46]
R Logesh and V Subramaniyaswamy. 2019. Exploring hybrid recommender systems for personalized travel applications. In Cognitive informatics and soft computing. Springer, 535--544.
[47]
R Logesh, V Subramaniyaswamy, V Vijayakumar, and Xiong Li. 2019. Efficient user profiling based intelligent travel recommender system for individual and group of users. Mobile Networks and Applications 24, 3 (2019), 1018--1033.
[48]
Yi-Shu Lu, Wen-Yueh Shih, Hung-Yi Gau, Kuan-Chieh Chung, and Jiun-Long Huang. 2018. On Successive Point-Of-Interest Recommendation. World Wide Web 22, 3 (2018), 1151--1173.
[49]
Ziyu Lu, Hao Wang, Nikos Mamoulis, Wenting Tu, and David W. Cheung. 2017. Personalized Location Recommendation By Aggregating Multiple Recommenders in Diversity. GeoInformatica 21, 3 (2017), 459--484.
[50]
Wenjing Luan, Guanjun Liu, Changjun Jiang, and Liang Qi. 2017. Partition-Based Collaborative Tensor Factorization for Poi Recommendation. IEEE/CAA Journal of Automatica Sinica 4, 3 (2017), 437--446.
[51]
Chen Ma, Yingxue Zhang, Qinglong Wang, and Xue Liu. 2018. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18. ACM Press.
[52]
David Massimo and Francesco Ricci. 2018. Harnessing a generalised user behaviour model for next-POI recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18.
[53]
Sean M. McNee, John Thomas Riedl, and Joseph A. Konstan. 2006. Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems.
[54]
Xiangfu Meng, Yanhuan Tang, and Xiaoyan Zhang. 2017. DP-POIRS: A Diversified and Personalized Point-of-Interest Recommendation System. In 2017 IEEE International Conference on Data Science and Advanced Analytics. 332--333.
[55]
Sara Migliorini, Damiano Carra, and Alberto Belussi. 2018. Adaptive Trip Recommendation System: Balancing Travelers among POIs with MapReduce. In 2018 IEEE International Congress on Big Data (BigData Congress).
[56]
Shokirkhon Oppokhonov, Seyoung Park, and Isaac K. E. Ampomah. 2017. Current location-based next POI recommendation. In Proceedings of the International Conference on Web Intelligence - WI'17.
[57]
Kai Petersen, Robert Feldt, Shahid Mujtaba, and Michael Mattsson. 2008. Systematic Mapping Studies in Software Engineering. In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (Italy) (EASE'08). BCS Learning & Development Ltd, Swindon, GBR, 68--77.
[58]
Tuan-Anh Nguyen Pham, Xutao Li, and Gao Cong. 2017. A General Model for Out-of-town Region Recommendation. In Proceedings of the 26th International Conference on World Wide Web - WWW '17.
[59]
Tieyun Qian, Bei Liu, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2019. Spatio-temporal Representation Learning for Translation-Based Poi Recommendation. ACM Transactions on Information Systems 37, 2 (2019), 1--24.
[60]
Vineeth Rakesh, Niranjan Jadhav, Alexander Kotov, and Chandan K. Reddy. 2017. Probabilistic Social Sequential Model for Tour Recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17.
[61]
Xingyi Ren, Meina Song, Haihong E, and Junde Song. 2017. Context-Aware Probabilistic Matrix Factorization Modeling for Point-Of-Interest Recommendation. Neurocomputing 241, nil (2017), 38--55.
[62]
J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The adaptive web. Springer, 291--324.
[63]
Yali Si, Fuzhi Zhang, and Wenyuan Liu. 2017. Ctf-Ara:an Adaptive Method for Poi Recommendation Based on Check-In and Temporal Features. Knowledge-Based Systems 128, nil (2017), 59--70.
[64]
Yali Si, Fuzhi Zhang, and Wenyuan Liu. 2019. An Adaptive Point-Of-Interest Recommendation Method for Location-Based Social Networks Based on User Activity and Spatial Features. Knowledge-Based Systems 163, nil (2019), 267--282.
[65]
Barry Smyth and Paul McClave. 2001. Similarity vs. diversity. In International Conference on Case-Based Reasoning. Springer, 347--361.
[66]
Chuang Song, Junhao Wen, and Shun Li. 2019. Personalized POI recommendation based on check-in data and geographical-regional influence. In Proceedings of the 3rd International Conference on Machine Learning and Soft Computing - ICMLSC 2019.
[67]
V. Vijayakumar, Subramaniyaswamy Vairavasundaram, R. Logesh, and A. Sivapathi. 2019. Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation. International Journal of Web Portals 11, 1 (2019), 1--18.
[68]
Hao Wang, Yanmei Fu, Qinyong Wang, Hongzhi Yin, Changying Du, and Hui Xiong. 2017. A location-sentiment-aware recommender system for both hometown and out-of-town users. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135--1143.
[69]
Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng. 2018. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.
[70]
Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu. 2017. What Your Images Reveal. In Proceedings of the 26th International Conference on World Wide Web - WWW 17.
[71]
Shuning Xing, Fangai Liu, Xiaohui Zhao, and Tianlai Li. 2017. Points-Of-Interest Recommendation Based on Convolution Matrix Factorization. Applied Intelligence 48, 8 (2017), 2458--2469.
[72]
Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan, and Jiawei Han. 2017. Bridging Collaborative Filtering and Semi-Supervised Learning. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17.
[73]
Zijun Yao. 2018. Exploiting Human Mobility Patterns for Point-of-Interest Recommendation. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18.
[74]
Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 325--334.
[75]
Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017. Spatial-Aware Hierarchical Collaborative Deep Learning for Poi Recommendation. IEEE Transactions on Knowledge and Data Engineering 29, 11 (2017), 2537--2551.
[76]
Haochao Ying, Jian Wu, Guandong Xu, Yanchi Liu, Tingting Liang, Xiao Zhang, and Hui Xiong. 2018. Time-Aware Metric Embedding With Asymmetric Projection for Successive Poi Recommendation. World Wide Web 22, 5 (2018), 2209--2224.
[77]
Yuankai Ying, Ling Chen, and Gencai Chen. 2017. A Temporal-Aware Poi Recommendation System Using Context-Aware Tensor Decomposition and Weighted Hits. Neurocomputing 242, nil (2017), 195--205.
[78]
Yonghong Yu and Xingguo Chen. 2015. A survey of point-of-interest recommendation in location-based social networks. In Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence.
[79]
Mi Zhang and Neil Hurley. 2008. Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems. ACM, 123--130.
[80]
Zhiyuan Zhang, Yun Liu, Zhenjiang Zhang, and Bo Shen. 2018. Fused Matrix Factorization With Multi-Tag, Social and Geographical Influences for Poi Recommendation. World Wide Web 22, 3 (2018), 1135--1150.
[81]
Pengpeng Zhao, Xiefeng Xu, Yanchi Liu, Ziting Zhou, Kai Zheng, Victor S. Sheng, and Hui Xiong. 2017. Exploiting Hierarchical Structures for POI Recommendation. In 2017 IEEE International Conference on Data Mining (ICDM).
[82]
Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Zhixu Li, Jiajie Xu, and Victor S. Sheng. 2018. Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation. ArXiv abs/1806.06671 (2018).
[83]
Peng-Peng Zhao, Hai-Feng Zhu, Yanchi Liu, Zi-Ting Zhou, Zhi-Xu Li, Jia-Jie Xu, Lei Zhao, and Victor S. Sheng. 2018. A Generative Model Approach for Geo-Social Group Recommendation. Journal of Computer Science and Technology 33, 4 (2018), 727--738.
[84]
Shenglin Zhao, Irwin King, and Michael R. Lyu. 2017. Aggregated Temporal Tensor Factorization Model for Point-Of-Interest Recommendation. Neural Processing Letters 47, 3 (2017), 975--992.
[85]
Shenglin Zhao, Michael R. Lyu, and Irwin King. 2018. Geo-Teaser: Geo-Temporal Sequential Embedding Rank for POI Recommendation. Springer Singapore, 57--78.
[86]
Shenglin Zhao, Michael R. Lyu, and Irwin King. 2018. STELLAR: Spatial-Temporal Latent Ranking Model for Successive POI Recommendation. Springer Singapore.
[87]
Shenglin Zhao, Tong Zhao, Irwin King, and Michael R Lyu. 2017. Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In Proceedings of the 26th Intl conference on world wide web companion. 153--162.
[88]
Xiangguo Zhao, Zhongyu Ma, and Zhen Zhang. 2017. A Novel Recommendation System in Location-Based Social Networks Using Distributed Elm. Memetic Computing 10, 3 (2017), 321--331.
[89]
Fan Zhou, Ruiyang Yin, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Jin Wu. 2019. Adversarial Point-of-Interest Recommendation. In The World Wide Web Conference on - WWW '19.
[90]
Guoqiang Zhou, Shuai Zhang, Yi Fan, Jingjin Li, Wenbo Yao, and Hongfang Liu. 2019. Recommendations based on user effective point-of-interest path. International Journal of Machine Learning and Cybernetics 10, 10 (2019).
[91]
Qiliang Zhu, Shangguang Wang, Bo Cheng, Qibo Sun, Fangchun Yang, and Rong N. Chang. 2018. Context-Aware Group Recommendation for Point-Of-Interests. IEEE Access 6, nil (2018), 12129--12144.

Cited By

View all
  • (2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
  • (2024)Negative Sampling in Next-POI Recommendations: Observation, Approach, and EvaluationProceedings of the ACM Web Conference 202410.1145/3589334.3645681(3888-3899)Online publication date: 13-May-2024
  • (2024)A Comprehensive Survey on Deep Graph Representation LearningNeural Networks10.1016/j.neunet.2024.106207173(106207)Online publication date: May-2024
  • Show More Cited By

Index Terms

  1. A Survey on Point-of-Interest Recommendation in Location-based Social Networks

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WebMedia '20: Proceedings of the Brazilian Symposium on Multimedia and the Web
    November 2020
    364 pages
    ISBN:9781450381963
    DOI:10.1145/3428658
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    In-Cooperation

    • SBC: Brazilian Computer Society
    • CNPq: Conselho Nacional de Desenvolvimento Cientifico e Tecn
    • CGIBR: Comite Gestor da Internet no Brazil
    • CAPES: Brazilian Higher Education Funding Council

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 November 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. POI Recommendation
    2. Recommender Systems
    3. Survey

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    WebMedia '20
    Sponsor:
    WebMedia '20: Brazillian Symposium on Multimedia and the Web
    November 30 - December 4, 2020
    São Luís, Brazil

    Acceptance Rates

    WebMedia '20 Paper Acceptance Rate 34 of 87 submissions, 39%;
    Overall Acceptance Rate 270 of 873 submissions, 31%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)69
    • Downloads (Last 6 weeks)6
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Latent Representation Learning for Geospatial EntitiesACM Transactions on Spatial Algorithms and Systems10.1145/366347410:4(1-31)Online publication date: 2-May-2024
    • (2024)Negative Sampling in Next-POI Recommendations: Observation, Approach, and EvaluationProceedings of the ACM Web Conference 202410.1145/3589334.3645681(3888-3899)Online publication date: 13-May-2024
    • (2024)A Comprehensive Survey on Deep Graph Representation LearningNeural Networks10.1016/j.neunet.2024.106207173(106207)Online publication date: May-2024
    • (2024)HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalised POI recommendationExpert Systems with Applications10.1016/j.eswa.2024.125217(125217)Online publication date: Aug-2024
    • (2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
    • (2024)A survey on graph neural network-based next POI recommendation for smart citiesJournal of Reliable Intelligent Environments10.1007/s40860-024-00233-z10:3(299-318)Online publication date: 26-Jul-2024
    • (2024)Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive reviewGeoInformatica10.1007/s10707-024-00531-xOnline publication date: 28-Oct-2024
    • (2024)Secure Federated Matrix Factorization via Shuffling Encrypted Parameters Between DevicesAdvances in Intelligent Networking and Collaborative Systems10.1007/978-3-031-72322-3_11(107-119)Online publication date: 15-Sep-2024
    • (2023)A Survey on Review - Aware Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617050(198-207)Online publication date: 23-Oct-2023
    • (2023)A Modular Social Sensing System for Personalized Orienteering in the COVID-19 EraACM Transactions on Management Information Systems10.1145/361535914:4(1-26)Online publication date: 26-Oct-2023
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media