Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Diversified Interactive Recommendation with Implicit Feedback
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase DPP quality weight alpha beyond 5 (to 10 and 100) (ablation) | DC2B · ML-100K | Precision@50 | Hurt |
| increase alpha (quality weight) (ablation) | DC2B · ML-100K | Diversity | Hurt |
| increase alpha from 0.01 up to about 5 (ablation) | DC2B · ML-100K | Precision@50 | Helped |
| larger super arm size (10 vs 5) (ablation) | DC2B · ML-100K | Diversity (ILD): 0.8110 -> 0.8118 | No clear effect |
| larger super arm size (10 vs 5) (ablation) | DC2B · ML-100K | Precision | Helped |
| DC2B (DPP + Bayesian bandit) instead of EC-Bandit | interactive recommenders · Anime | Precision@50: 0.1168 -> 0.2419 | Helped |
| DC2B instead of C2UCB | interactive recommenders · ML-100K | Precision@50: 0.3146 -> 0.2882 | Hurt |
| DC2B instead of C2UCB | interactive recommenders · Anime | F-measure: 0.2332 -> 0.3752 | Helped |
| DC2B instead of non-interactive diversity methods (MMR) | recommenders · Anime | Diversity (ILD): 0.8495 -> 0.8355 | Hurt |
Closest papers by meaning
- A Hybrid Bandit Framework for Diversified Recommendation (AAAI 2021 · similarity 0.89)
- Factorization Bandits for Interactive Recommendation (AAAI 2017 · similarity 0.88)
- Transferable Contextual Bandit for Cross-Domain Recommendation (AAAI 2018 · similarity 0.87)
- Tree of Preferences for Diversified Recommendation (NeurIPS 2025 · similarity 0.87)
- Gaussian Process Bandits for Top-k Recommendations (NeurIPS 2024 · similarity 0.84)
- Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness (NeurIPS 2026 · similarity 0.84)
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