research-article

Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy

Authors:

Jesse Anderton,

Kevin Jamieson,

Hugues Bouchard,

Javed AslamAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 134 - 144

https://doi.org/10.1145/3523227.3546766

Published: 13 September 2022 Publication History

Abstract

Podcasting is an increasingly popular medium for entertainment and discourse around the world, with tens of thousands of new podcasts released on a monthly basis. We consider the problem of identifying from these newly-released podcasts those with the largest potential audiences so they can be considered for personalized recommendation to users. We first study and then discard a supervised approach due to the inadequacy of either content or consumption features for this task, and instead propose a novel non-contextual bandit algorithm in the fixed-budget infinitely-armed pure-exploration setting. We demonstrate that our algorithm is well-suited to the best-arm identification task for a broad class of arm reservoir distributions, out-competing a large number of state-of-the-art algorithms. We then apply the algorithm to identifying podcasts with broad appeal in a simulated study, and show that it efficiently sorts podcasts into groups by increasing appeal while avoiding the popularity bias inherent in supervised approaches.

Supplementary Material

MP4 File (Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy.mp4)

Presentation video

Download
211.77 MB

References

[1]

Jean-Yves Audibert and Sébastien Bubeck. 2010. Best arm identification in multi-armed bandits. In COLT-23th Conference on Learning Theory-2010. 13–p.

[2]

Maryam Aziz, Jesse Anderton, Emilie Kaufmann, and Javed Aslam. 2018. Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence. In ALT 2018-Algorithmic Learning Theory.

[3]

Mathias Bärtl. 2018. YouTube channels, uploads and views: A statistical analysis of the past 10 years. Convergence 24, 1 (2018), 16–32.

[4]

Donald A. Berry, Robert W. Chen, Alan Zame, David C. Heath, and Larry A. Shepp. 1997. Bandit problems with infinitely many arms. The Annals of Statistics 25, 5 (10 1997), 2103–2116.

[5]

Thomas Bonald and Alexandre Proutiere. 2013. Two-target algorithms for infinite-armed bandits with Bernoulli rewards. In Advances in Neural Information Processing Systems. 2184–2192.

[6]

Sébastien Bubeck, Nicolo Cesa-Bianchi, 2012. Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends® in Machine Learning 5, 1(2012), 1–122.

[7]

Sébastien Bubeck, Rémi Munos, and Gilles Stoltz. 2009. Pure exploration in multi-armed bandits problems. In International conference on Algorithmic learning theory. Springer, 23–37.

[8]

Giuseppe Burtini, Jason Loeppky, and Ramon Lawrence. 2015. A survey of online experiment design with the stochastic multi-armed bandit. arXiv preprint arXiv:1510.00757(2015).

[9]

Alexandra Carpentier and Michal Valko. 2015. Simple regret for infinitely many armed bandits. CoRR abs/1505.04627(2015).

[10]

Hock Peng Chan and Shouri Hu. 2018. Infinite Arms Bandit: Optimality via Confidence Bounds. CoRR abs/1805.11793(2018).

[11]

Karthekeyan Chandrasekaran and Richard Karp. 2014. Finding a most biased coin with fewest flips. In Conference on Learning Theory. 394–407.

[12]

Arghya Roy Chaudhuri and Shivaram Kalyanakrishnan. 2017. PAC Identification of a Bandit Arm Relative to a Reward Quantile. In AAAI. 1777–1783.

[13]

Arghya Roy Chaudhuri and Shivaram Kalyanakrishnan. 2018. Quantile-Regret Minimisation in Infinitely Many-Armed Bandits. In UAI. 425–434.

[14]

Yahel David and Nahum Shimkin. 2014. Infinitely many-armed bandits with unknown value distribution. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 307–322.

Digital Library

[15]

Yahel David and Nahum Shimkin. 2015. Refined algorithms for infinitely many-armed bandits with deterministic rewards. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 464–479.

Digital Library

[16]

Flavio Figueiredo, Fabrício Benevenuto, and Jussara M Almeida. 2011. The tube over time: characterizing popularity growth of youtube videos. In Proceedings of the fourth ACM international conference on Web search and data mining. 745–754.

Digital Library

[17]

Rossano Gaeta, Michele Garetto, Giancarlo Ruffo, and Alessandro Flammini. 2022. Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets. ACM Transactions on Information Systems (TOIS) (2022).

[18]

F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4(2015), 1–19.

[19]

Kevin G Jamieson, Daniel Haas, and Benjamin Recht. 2016. The power of adaptivity in identifying statistical alternatives. In Advances in Neural Information Processing Systems. 775–783.

[20]

Kevin G Jamieson, Lalit Jain, Chris Fernandez, Nicholas J Glattard, and Rob Nowak. 2015. Next: A system for real-world development, evaluation, and application of active learning. Advances in neural information processing systems 28 (2015).

[21]

Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, and Sébastien Bubeck. 2014. lil’ UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. In COLT.

[22]

Zohar Karnin, Tomer Koren, and Oren Somekh. 2013. Almost Optimal Exploration in Multi-Armed Bandits. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), Vol. 28. 1238–1246.

Digital Library

[23]

Lisha Li, Kevin G Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2017. Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.Journal of Machine Learning Research 18 (2017), 185–1.

[24]

Larkin Liu, Richard Downe, and Joshua Reid. 2019. Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes. arXiv e-prints, Article arXiv:1902.08593 (Feb. 2019), arXiv:1902.08593 pages. arxiv:1902.08593 [cs.LG]

[25]

Ilias N Lymperopoulos. 2016. Predicting the popularity growth of online content: Model and algorithm. Information Sciences 369(2016), 585–613.

Digital Library

[26]

Matthew L Malloy, Gongguo Tang, and Robert D Nowak. 2012. Quickest search for a rare distribution. In Information Sciences and Systems (CISS), 2012 46th Annual Conference on. IEEE, 1–6.

[27]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[28]

Sebastian Pilarski, Slawomir Pilarski, and Dániel Varró. 2021. Delayed reward bernoulli bandits: Optimal policy and predictive meta-algorithm pardi. IEEE Transactions on Artificial Intelligence 3, 2(2021), 152–163.

[29]

Henrique Pinto, Jussara M Almeida, and Marcos A Gonçalves. 2013. Using early view patterns to predict the popularity of youtube videos. In Proceedings of the sixth ACM international conference on Web search and data mining. 365–374.

Digital Library

[30]

Wenbo Ren, Jia Liu, and Ness Shroff. 2018. Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits. arXiv preprint arXiv:1810.11857(2018).

[31]

Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. 2006. Experimental study of inequality and unpredictability in an artificial cultural market. science 311, 5762 (2006), 854–856.

[32]

Gabor Szabo and Bernardo A Huberman. 2010. Predicting the popularity of online content. Commun. ACM 53, 8 (2010), 80–88.

Digital Library

[33]

Olivier Teytaud, Sylvain Gelly, and Michele Sebag. 2007. Anytime many-armed bandits. In CAP07.

[34]

Yizao Wang, Jean-Yves Audibert, and Rémi Munos. 2008. Algorithms for infinitely many-armed bandits. Advances in Neural Information Processing Systems 21 (2008).

Cited By

Bhuiyan MHu DJelson AMitra TLee S(2024)Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblankProceedings of the ACM on Human-Computer Interaction10.1145/36870418:CSCW2(1-23)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687041
Dinnissen K(2024)Fairness and Transparency in Music Recommender Systems: Improvements for ArtistsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688024(1368-1375)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688024
Aziz MAnderton JJamieson KWang ABouchard HAslam J(2024)Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit StrategyACM Transactions on Recommender Systems10.1145/36263243:1(1-22)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3626324
Show More Cited By

Index Terms

Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

Recommendations

Pure exploration in finitely-armed and continuous-armed bandits

We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of forecasters that perform an on-line exploration of the arms. These forecasters are assessed in terms of their simple regret, a regret ...
Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit Strategy
Podcasting is an increasingly popular medium for entertainment and discourse around the world, with tens of thousands of new podcasts released on a monthly basis. We consider the problem of identifying from these newly released podcasts those with the ...
Multi-armed bandit problem with known trend

We consider a variant of the multi-armed bandit model, which we call multi-armed bandit problem with known trend, where the gambler knows the shape of the reward function of each arm but not its distribution. This new problem is motivated by different ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article
Research
Refereed limited

Conference

RecSys '22

Sponsor:

RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
1,004
Total Downloads

Downloads (Last 12 months)60
Downloads (Last 6 weeks)7

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Bhuiyan MHu DJelson AMitra TLee S(2024)Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblankProceedings of the ACM on Human-Computer Interaction10.1145/36870418:CSCW2(1-23)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687041
Dinnissen K(2024)Fairness and Transparency in Music Recommender Systems: Improvements for ArtistsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688024(1368-1375)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688024
Aziz MAnderton JJamieson KWang ABouchard HAslam J(2024)Unbiased Identification of Broadly Appealing Content Using a Pure Exploration Infinitely Armed Bandit StrategyACM Transactions on Recommender Systems10.1145/36263243:1(1-22)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3626324
Tian YAziz MWang APalumbo EBouchard HChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Structural Podcast Content Modeling with GeneralizabilityCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3651563(710-713)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3651563
Zhao YStephens CSzepesvári CJun KKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Revisiting simple regret: fast rates for returning a good armProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3620180(42110-42158)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3620180
Baran BJunior GDanylenko AFolorunso OForsum GLefarov MMaystre LZhao Y(2023)Accelerating Creator Audience Building through Centralized ExplorationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608880(70-73)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608880
McDonald TMaystre LLalmas MRusso DCiosek KSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Impatient Bandits: Optimizing Recommendations for the Long-Term Without DelayProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599386(1687-1697)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599386

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten