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Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation.
- Source :
-
Expert Systems with Applications . Jul2024, Vol. 246, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- This paper discusses the use of portfolio approaches based on bandit algorithms to optimize multicriteria decision-making in recommender systems (accuracy and diversity). While previous research has primarily focused on single-item recommendations, this study extends the research to consider the recommendation of several items per iteration. Two methods, Multiple-play Gorthaur and Budgeted-Gorthaur, are proposed to solve the algorithm selection problem and their performances on real-world datasets are compared. Both methods provide a generalization of the Gorthaur method, which enables it to operate with any Multi-Armed Bandit (MAB) and Contextual Multi-Armed Bandit (CMAB) algorithm as meta-algorithm in a multi-item recommendation scenario. For Multiple-play Gorthaur, an empirical evaluation shows that the use of Thompson Sampling for algorithm selection (Gorthaur-TS) yields better results than the original EXP3 method (Gorthaur-EXP3) and the exclusive use of the optimal algorithm in the portfolio in contextual recommendation problems. Additionally, the paper includes a theoretical regret analysis based on the TS sketch proof applied for this variant of the method. Concerning Budgeted-Gorthaur, experiments show that it allows more flexibility to achieve a suitable trade-off between criteria and a broader coverage of the Pareto set of solutions, overcoming a natural limit of "a-priori" methods. Finally, this paper provides a detailed review, including pseudocodes and theoretical bounds, for all the fundamental MAB and CMAB algorithms used in this study. • Bandit literature lacks formal algorithm review, hindering clarity and comparability. • There is no silver bullet: no algorithm can be the best performer in every instance. • Recommender systems need to balance accuracy, diversity, multi-item recommendations. • Optimal algorithm balances criteria, matching decision maker's preferred trade-off. • Dynamic selection ensures safe performance when optimal algorithm is unknown. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RECOMMENDER systems
*FUZZY sets
*ALGORITHMS
*REINFORCEMENT learning
Subjects
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 246
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 176225972
- Full Text :
- https://doi.org/10.1016/j.eswa.2024.123151