Summary of Comparative Performance Of Collaborative Bandit Algorithms: Effect Of Sparsity and Exploration Intensity, by Eren Ozbay
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity
by Eren Ozbay
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a comprehensive analysis of collaborative bandit algorithms, comparing their performance. Collaborative bandits improve contextual bandits by introducing relationships between arms, enabling information propagation. By collaborating, arms can share feedback from a single user across related users, alleviating the cold start problem. The study focuses on soft clustering, modeling absolute relationships between arms as binary or fuzzy assignments. Extensive experiments are conducted on state-of-the-art collaborative contextual bandit algorithms, investigating sparsity and exploration intensity effects. Results show that controlling for sparsity improves data efficiency and performance, while increasing exploration intensity acts as a correction mechanism reducing variance due to misspecified relationships. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special types of decision-making algorithms called “collaborative bandits.” These algorithms are used in situations where many things (like users or items) are being judged based on how well they perform. The goal is to make good decisions by sharing information between related things. This helps solve a problem called the “cold start” where we don’t have enough information about new things. The researchers focus on one way of modeling these relationships, called “soft clustering.” They test different approaches and find that controlling for how sparse or scattered the data is makes it better. They also discover that increasing how much they explore (or try out) different options helps correct mistakes. |
Keywords
» Artificial intelligence » Clustering
