research-article

GeoLifecycle: User Engagement of Geographical Exploration and Churn Prediction in LBSNs

Authors:

Dimitris Chatzopoulos,

Raymond Chi-Wing Wong,

Pan HuiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 3

Article No.: 92, Pages 1 - 29

https://doi.org/10.1145/3351250

Published: 09 September 2019 Publication History

Abstract

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.

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Yin Zhu, Erheng Zhong, Sinno Jialin Pan, Xiao Wang, Minzhe Zhou, and Qiang Yang. 2013. Predicting User Activity Level in Social Networks. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management (CIKM '13). ACM, New York, NY, USA, 159--168.

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Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
Li WZheng WXiao XWang S(2023)STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based RepresentationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608796(602-612)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608796
Zhang GZeng JZhao ZJin DLi YSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)A Counterfactual Modeling Framework for Churn PredictionProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498468(1424-1432)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498468
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GeoLifecycle: User Engagement of Geographical Exploration and Churn Prediction in LBSNs

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 3

September 2019

1415 pages

EISSN:2474-9567

DOI:10.1145/3361560

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Copyright © 2019 ACM.

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]

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Published: 09 September 2019

Published in IMWUT Volume 3, Issue 3

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Cited By

Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
Li WZheng WXiao XWang S(2023)STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based RepresentationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608796(602-612)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608796
Zhang GZeng JZhao ZJin DLi YSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)A Counterfactual Modeling Framework for Churn PredictionProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498468(1424-1432)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498468
Vallapuram AKwon YLee LXu FHui P(2022)Causal Analysis on the Anchor Store Effect in a Location-based Social Network2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM55673.2022.10068687(202-209)Online publication date: 10-Nov-2022
https://doi.org/10.1109/ASONAM55673.2022.10068687
Kim SLee H(2022)Customer Churn Prediction in Influencer Commerce: An Application of Decision TreesProcedia Computer Science10.1016/j.procs.2022.01.169199(1332-1339)Online publication date: 2022
https://doi.org/10.1016/j.procs.2022.01.169
Vallapuram ANanda NKwon YHui PKlamma RO'Halloran SRokne J(2021)Interpretable business survival predictionProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3488353(99-106)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3487351.3488353
Chen LChen YKwon YKang YHui PKlamma RO'Halloran SRokne J(2021)IANProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3488328(23-30)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3487351.3488328
Hadi Mogavi RMa XHui P(2021)Characterizing Student Engagement Moods for Dropout Prediction in Question Pool WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/34490865:CSCW1(1-22)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449086
Li WXu ZSun YGong QChen YDing AWang XHui P(2021)DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable FeaturesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3091503(1-1)Online publication date: 2021
https://doi.org/10.1109/TKDE.2021.3091503
Chen YHu JXiao YLi XHui P(2020)Understanding the User Behavior of Foursquare: A Data-Driven Study on a Global ScaleIEEE Transactions on Computational Social Systems10.1109/TCSS.2020.29922947:4(1019-1032)Online publication date: Aug-2020
https://doi.org/10.1109/TCSS.2020.2992294
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