IAN: Interpretable attention network for churn prediction in LBSNs
Authors
Chen, LY
Chen, Y
Kwon, YD
Kang, Y
Hui, P
Publication Date
2021-11-08Journal Title
Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
Conference Name
ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining
ISBN
9781450391283
Publisher
ACM
Pages
23-30
Type
Conference Object
This Version
VoR
Metadata
Show full item recordCitation
Chen, L., Chen, Y., Kwon, Y., Kang, Y., & Hui, P. (2021). IAN: Interpretable attention network for churn prediction in LBSNs. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021, 23-30. https://doi.org/10.1145/3487351.3488328
Abstract
With the rise of Location-Based Social Networks (LBSNs) and their heavy reliance on User-Generated Content, it has become essential to attract and keep more users, which makes the churn prediction problem interesting. Recent research focuses on solving the task by utilizing complex neural networks. However, due to the black-box nature of those proposed deep learning algorithms, it is still a challenge for LBSN managers to interpret the prediction results and design strategies to prevent churning behavior. Therefore, in this paper, we perform the first investigation into the interpretability of the churn prediction in LBSNs. We proposed a novel attention-based deep learning network, Interpretable Attention Network (IAN), to achieve high performance while ensuring interpretability. The network is capable to process the complex temporal multivariate multidimensional user data from LBSN datasets (i.e. Yelp and Foursquare) and provides meaningful explanations of its prediction. We also utilize several visualization techniques to interpret the prediction results. By analyzing the attention output, researchers can intuitively gain insights into which features dominate the model's prediction of churning users. Finally, we expect our model to become a robust and powerful tool to help LBSN applications to understand and analyze user churning behavior and in turn remain users.
Embargo Lift Date
2100-01-01
Identifiers
External DOI: https://doi.org/10.1145/3487351.3488328
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334124
Rights
Publisher's own licence
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