Adapting bandit algorithms for settings with sequentially available arms

Gabrielli, Marco; Antonelli, Manuela; Trovò, Francesco

doi:10.1016/j.engappai.2023.107815

Cited by 2 publications

(1 citation statement)

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“…It is noted that the TS method is used to a collection of search and optimization "bandits" in Multiarmed bandits, Thomson sampling, and unsupervised machine learning in phylogenetic graph search in order to favor fruitful search strategies [9]. Without any prior understanding of the attributes of the phylogenetic datasets, this technique functions as a type of unsupervised machine learning.…”

Section: Ts Algorithmmentioning

confidence: 99%

Analyzing the strengths and weaknesses of diverse algorithms for solving Multi-Armed Bandit problems using Python

Zhang

2024

ACE

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With the rapid advancement of science and technology, the internet has become an integral part of daily life, revolutionizing how people access information and make decisions. In this context, algorithms play a pivotal role in helping individuals make informed choices tailored to their preferences across various domains. Utilizing the MovieLens dataset (https://grouplens.org/datasets/movielens/1m/), which contains a rich compilation of movie ratings and metadata, this study conducts a thorough analysis using Python to assess the performance of four distinct algorithms: Explore-then-Commit (ETC), Upper Confidence Bound (UCB), Thompson Sampling (TS), and Epsilon-Greedy. The comparison reveals that the ETC algorithm excels in applications such as online advertising recommendation and autonomous driving. The UCB algorithm proves more advantageous in financial analysis, where risk management is critical. The TS algorithm is particularly effective in short video recommendation systems, while the Epsilon-Greedy algorithm is well-suited for balancing exploration with reward. Overall, the results indicate that the TS algorithm outperforms the others in general efficacy.

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Section: Ts Algorithmmentioning

confidence: 99%