Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming
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1803.01440.pdf
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Author(s)
Zhu, Zhen
Vahabi, Hossein
Dedieu, Antoine
Mazumder, Rahul
Date Issued
October 2018
Journal
Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18
Publisher
Association for Computer Machinery
Citation
Dedieu, Antoine, Rahul Mazumder, Zhen Zhu, and Hossein Vahabi. “Hierarchical Modeling and Shrinkage for User Session LengthPrediction in Media Streaming.” Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM ’18, 22-26 October, 2018, Torino, Italy, ACM, 2018.
Version
Original manuscript
Abstract
An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video streaming services. Recent research has shown that predicting the exact amount of time spent is highly nontrivial due to many external factors for which a user can end a session, and the lack of predictive covariates. Most of the other related literature on duration based user engagement has focused on dwell time for websites, for search and display ads, mainly for post-click satisfaction prediction or ad ranking. In this work we present a novel framework inspired by hierarchical Bayesian modeling to predict, at the moment of login, the amount of time a user will spend in the streaming service. The time spent by a user on a platform depends upon user-specific latent variables which are learned via hierarchical shrinkage. Our framework enjoys theoretical guarantees and naturally incorporates flexible parametric/nonparametric models on the covariates, including models robust to outliers. Our proposal is found to outperform state-of-the-art estimators in terms of efficiency and predictive performance on real world public and private datasets.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3269206.3271700
