research-article

A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

Authors:

Crícia Z. Felício,

Klérisson V.R. Paixão,

Celia A.Z. Barcelos,

Philippe PreuxAuthors Info & Claims

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

Pages 32 - 40

https://doi.org/10.1145/3079628.3079681

Published: 09 July 2017 Publication History

Abstract

How can we effectively recommend items to a user about whom we have no information? This is the problem we focus on in this paper, known as the cold-start problem. In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information? Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming that a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS. In comparison with three baselines, PdMS improves the performance as measured by the nDCG. These improvements are demonstrated on real, public datasets.

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Cited By

Singh NSingh S(2024)Reinforcement Learning in Bug TriagingAdvancing Software Engineering Through AI, Federated Learning, and Large Language Models10.4018/979-8-3693-3502-4.ch011(162-182)Online publication date: 21-Jun-2024
https://doi.org/10.4018/979-8-3693-3502-4.ch011
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Singh NSingh S(2024)A systematic literature review of solutions for cold start problemInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02359-y15:7(2818-2852)Online publication date: 14-May-2024
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Index Terms

A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

UMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

July 2017

420 pages

ISBN:9781450346351

DOI:10.1145/3079628

General Chairs:
Maria Bielikova
Slovak University of Technology in Bratislava, Slovakia
,
Eelco Herder
L3S Research Center, Germany and Radboud Universiteit Nijmegen, The Netherlands
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Michel Desmarais
Polytechnique University, Montreal, Canada

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Published: 09 July 2017

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UMAP '17

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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization

July 9 - 12, 2017

Bratislava, Slovakia

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UMAP '17 Paper Acceptance Rate 29 of 80 submissions, 36%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Singh NSingh S(2024)Reinforcement Learning in Bug TriagingAdvancing Software Engineering Through AI, Federated Learning, and Large Language Models10.4018/979-8-3693-3502-4.ch011(162-182)Online publication date: 21-Jun-2024
https://doi.org/10.4018/979-8-3693-3502-4.ch011
Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002
Singh NSingh S(2024)A systematic literature review of solutions for cold start problemInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02359-y15:7(2818-2852)Online publication date: 14-May-2024
https://doi.org/10.1007/s13198-024-02359-y
Singh NSingh S(2023)An Empirical Assessment of the Performance of Multi-Armed Bandits and Contextual Multi-Armed Bandits in Handling Cold-Start BugsProceedings of the 2023 Fifteenth International Conference on Contemporary Computing10.1145/3607947.3608094(750-758)Online publication date: 3-Aug-2023
https://dl.acm.org/doi/10.1145/3607947.3608094
Silva NSilva TWerneck HRocha LPereira A(2023)User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User’s ExperienceACM Transactions on Recommender Systems10.1145/35548191:1(1-24)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3554819
Silva NSilva THott HRibeiro YPereira ARocha LChen HDuh WHuang HKato MMothe JPoblete B(2023)Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591684(1178-1187)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591684
Tang YHou JHuang XShao ZYang Y(2023)Green Edge Intelligence Scheme for Mobile Keyboard Emoji PredictionIEEE Transactions on Mobile Computing10.1109/TMC.2023.3243955(1-13)Online publication date: 2023
https://doi.org/10.1109/TMC.2023.3243955
Miyake YMine T(2023)Contextual and Nonstationary Multi-armed Bandits Using the Linear Gaussian State Space Model for the Meta-Recommender System2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394517(3138-3145)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394517
Lucas Jde Figueiredo L(2023)Challenges and Solutions in Large-Scale News RecommendersHighlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection10.1007/978-3-031-37593-4_14(170-181)Online publication date: 8-Jul-2023
https://doi.org/10.1007/978-3-031-37593-4_14
Rajapakse DLeith D(2022)Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree SearchProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546786(350-359)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546786
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