Summary of Expert with Clustering: Hierarchical Online Preference Learning Framework, by Tianyue Zhou et al.
Expert with Clustering: Hierarchical Online Preference Learning Framework
by Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu
First submitted to arxiv on: 26 Jan 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 The proposed framework, Expert with Clustering (EWC), integrates clustering techniques and prediction with expert advice to accelerate user preference learning in emerging mobility systems. The EWC algorithm efficiently utilizes hierarchical user information and incorporates a novel Loss-guided Distance metric to generate more representative cluster centroids. In a recommendation scenario, the algorithm achieves a regret bound of O(N√TlogK + NT), consisting of two parts: the first term is the regret from the Hedge algorithm, and the second term depends on the average loss from clustering. Theoretical analysis demonstrates the efficacy of EWC in scenarios requiring rapid learning and adaptation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help people find the best transportation options. This method learns what people like by looking at their behavior and grouping them into categories. It’s very good at making recommendations, which is important because transportation choices can have big effects on people’s lives. The algorithm also tries to make sure that people are happy with their choices, so it adjusts its suggestions based on how well they work out. In experiments, this method did much better than others, reducing regret by 27.57%. This could be useful in many situations where there are layers of information and user preferences. |
Keywords
* Artificial intelligence * Clustering
