Summary of Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications, by Luyue Xu et al.
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications
by Luyue Xu, Liming Wang, Hong Xie, Mingqiang Zhou
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel variant of contextual bandits to address the “herding effects” in user feedback, which bias ratings towards historical behavior. The proposed algorithm, TS-Conf (Thompson Sampling under Conformity), uses posterior sampling to balance exploration and exploitation. It is shown that TS-Conf outperforms four benchmark algorithms on various datasets, mitigating the negative impact of herding effects and leading to faster learning and improved recommendation accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better recommendations online by fixing a problem in how we think about user feedback. Right now, people tend to give ratings that are similar to what others have given before, which can be misleading. The new algorithm, called TS-Conf, is designed to handle this issue and provide more accurate recommendations. |
