Summary of Contextual Multinomial Logit Bandits with General Value Functions, by Mengxiao Zhang et al.
Contextual Multinomial Logit Bandits with General Value Functions
by Mengxiao Zhang, Haipeng Luo
First submitted to arxiv on: 12 Feb 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 In this paper, researchers address limitations in contextual multinomial logit (MNL) bandits, which are commonly used to recommend products online. Existing work only considers linear value functions, restricting its application. To overcome this, the authors propose a new approach that incorporates general value functions, inspired by recent studies on contextual bandits. The paper presents algorithms for both stochastic and adversarial settings, offering different computation-regret trade-offs. When applied to the linear case, these results provide improved performance, including computational efficiency, dimension-free regret bounds, and the ability to handle completely adversarial contexts and rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better recommend products online by improving a type of decision-making algorithm called contextual multinomial logit (MNL) bandits. Right now, this algorithm only works well with simple value systems. The researchers in this study try to make it work with more complex value systems. They create new algorithms for two different situations: when the recommendations are based on chance and when they’re based on trying to trick the system. These new algorithms can handle tough situations where the value of a product changes quickly or is completely unpredictable. |
