Summary of Meta Clustering Of Neural Bandits, by Yikun Ban et al.
Meta Clustering of Neural Bandits
by Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, Jingrui He
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Clustering of Neural Bandits paper proposes a novel algorithm called M-CNB to recommend items in a sequential decision-making process. By extending previous work on contextual bandits, this approach balances user heterogeneity and correlations. The algorithm utilizes a meta-learner to adapt to dynamic clusters and an Upper Confidence Bound (UCB)-based exploration strategy for efficient exploration. This paper provides an instance-dependent performance guarantee that withstands adversarial contexts and outperforms state-of-the-art baselines in both recommendation and online classification scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new algorithm called M-CNB to help recommend items that people will like. They wanted to make sure the recommendations take into account how different people might have different preferences, while also considering the relationships between what people like and dislike. The algorithm uses two main parts: one helps figure out which groups of people are most similar, and another part decides which items to suggest based on those groups. The team tested their algorithm and found that it worked better than other approaches at recommending things people would enjoy. |
Keywords
* Artificial intelligence * Classification * Clustering
