Summary of Neural Combinatorial Clustered Bandits For Recommendation Systems, by Baran Atalar et al.
Neural Combinatorial Clustered Bandits for Recommendation Systems
by Baran Atalar, Carlee Joe-Wong
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 propose Neural UCB Clustering (NeUClust), a novel approach to solve the contextual combinatorial bandit problem. This problem involves selecting a subset of arms that maximize rewards while learning unknown reward functions. Conventional methods rely on restrictive models for reward functions, but NeUClust uses deep neural networks to estimate and learn these functions. The algorithm adopts a clustering approach to select the super arm in every round by exploiting underlying structure in the context space. Unlike prior works, NeUClust eliminates the need for a known optimization oracle by using a neural network to estimate the super arm reward and select it. The authors prove that NeUClust achieves () regret, where is the effective dimension of a neural tangent kernel matrix, T the number of rounds. Experimental results on real-world recommendation datasets show that NeUClust outperforms other contextual combinatorial and neural bandit algorithms in terms of regret and reward. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to solve a tricky problem called the contextual combinatorial bandit. This problem is like trying to find the best combination of products for someone who likes certain things. They used special computers to learn how to pick the right products without knowing what people will like in advance. The new method is called NeUClust and it’s better than other methods because it can figure out what people will like even if they don’t know beforehand. |
Keywords
» Artificial intelligence » Clustering » Neural network » Optimization
