Research map / Bandits and online learning / Contextual bandits and Thompson sampling
A Hybrid Bandit Framework for Diversified Recommendation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn personalized preferences for both item relevance and set diversity | LMDB · Movielens-100K, Movielens-1M, and Anime | Recall | Helped |
| Learn personalized preferences for both item relevance and set diversity | LMDB · Movielens-100K, Movielens-1M, and Anime | F1-score | Helped |
| Learn personalized preferences for both item relevance and set diversity | LMDB · Movielens-100K, Movielens-1M, and Anime | F2-score | Helped |
| Use LMDB instead of C2UCB | Bandit recommendation · Movielens-100K, Movielens-1M, and Anime | Diversity | Helped |
| Use LMDB instead of DC2B | Bandit recommendation · First 15 recommendation rounds | Diversity | Helped |
| Use LMDB instead of C2UCB or DC2B | Bandit recommendation · Simulated dataset | Regret | Helped |
| Set regularization parameter λ to values equal to or larger than 1 (ablation) | LMDB · Movielens-100K | F1-score | No clear effect |
| Set exploration parameter α to values equal to or larger than 1 (ablation) | LMDB · Movielens-100K | F1-score | No clear effect |
| Vary recommended set size across {5, 10, 15} (ablation) | LMDB · Movielens-100K | F1-score | No clear effect |
Closest papers by meaning
- Diversified Interactive Recommendation with Implicit Feedback (AAAI 2020 · similarity 0.89)
- Factorization Bandits for Interactive Recommendation (AAAI 2017 · similarity 0.88)
- Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests (AAAI 2020 · similarity 0.87)
- Tree of Preferences for Diversified Recommendation (NeurIPS 2025 · similarity 0.86)
- Building Higher-Order Abstractions from the Components of Recommender Systems (AAAI 2024 · similarity 0.85)
- DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation (AAAI 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.