ERIC Number: ED636016
Record Type: Non-Journal
Publication Date: 2023-Jun
Pages: 10
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Available Date: N/A
A Bandit You Can Trust
Ethan Prihar; Adam Sales; Neil Heffernan
Grantee Submission, Paper presented at the ACM Conference on User Modeling, Adaptation and Personalization (31st, Limassol, Cyprus, Jun 26-30, 2023)
This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our algorithm seeks to balance these objectives, allowing platforms to personalize content effectively while still gathering valuable data. Dynamic Linear Epsilon-Greedy was evaluated via simulation and an empirical study in the ASSISTments online learning platform. In simulation, Dynamic Linear Epsilon-Greedy performed comparably to existing algorithms and in ASSISTments, slightly increased students' learning compared to A/B testing. Data collected from its recommendations allowed for the identification of qualitative interactions, which showed high and low knowledge students benefited from different content. Dynamic Linear Epsilon-Greedy holds promise as a method to balance personalization with unbiased statistical analysis. All the data collected during the simulation and empirical study are publicly available at https://osf.io/zuwf7/. [This paper was published in: "UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization," June 26-29, 2023.]
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Institute of Education Sciences (ED); Department of Education (ED); Office of Elementary and Secondary Education (OESE) (ED), Education Innovation and Research (EIR); Office of Naval Research (ONR) (DOD); Federal Highway Administration (FHWA), National Highway Institute (NHI)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: 2118725; 2118904; 1950683; 1917808; 1931523; 1940236; 1917713; 1903304; 1822830; 1759229; 1724889; 1636782; 1535428; R305N210049; R305D210031; R305A170137; R305A170243; R305A180401; R305A120125; P200A180088; P200A150306; U411B190024; S411B210024; N000141812768; R44GM146483
Data File: URL: https://osf.io/zuwf7/
Author Affiliations: N/A