Summary of Hellinger-ucb: a Novel Algorithm For Stochastic Multi-armed Bandit Problem and Cold Start Problem in Recommender System, by Ruibo Yang et al.
HELLINGER-UCB: A novel algorithm for stochastic multi-armed bandit problem and cold start problem in recommender system
by Ruibo Yang, Jiazhou Wang, Andrew Mullhaupt
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a new variant of the Upper Confidence Bound (UCB) algorithm, called Hellinger-UCB, which leverages the squared Hellinger distance to build the upper confidence bound. The algorithm is designed to solve the stochastic multi-armed bandit problem, where the reward is driven by an unknown random variable. The authors prove that the Hellinger-UCB reaches the theoretical lower bound and has a solid statistical interpretation. They also demonstrate its effectiveness in finite time horizons through numerical experiments compared to other variants of the UCB algorithm. As a real-world application, the paper applies the Hellinger-UCB algorithm to solve the cold-start problem for a content recommender system of a financial app. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve a complex problem in machine learning. It’s called the Hellinger-UCB algorithm and it helps decide which option is best when you don’t know what will happen next. The authors show that their method works well and can be used to recommend things, like content for a financial app. They tested their algorithm and found it outperforms other methods in terms of how often people click on the recommended options. |
Keywords
» Artificial intelligence » Machine learning
