Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Learning from a Learning User for Optimal Recommendations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RAES to actively cut an uncertainty ellipsoid and diversify recommendations to improve user feedback | Recommendation with a learning user · Synthetic datasets | accumulated regret | Helped |
| Use DBGD instead of RAES | Recommendation with a learning user · Synthetic datasets with ill-posed V0 | accumulated regret | Hurt |
| Use Doubler or Sparring with an OFUL subroutine instead of RAES | Recommendation with a learning user · Synthetic datasets | accumulated regret | Hurt |
| Increase the user's learning-rate parameter γ | RAES · Synthetic datasets | accumulated regret | Hurt |
| Give the user a more informative prior V0 | RAES · Synthetic datasets | accumulated regret | Helped |
| Increase the problem dimension | RAES · Synthetic datasets | accumulated regret | Mixed |
Closest papers by meaning
- Learning the Optimal Recommendation from Explorative Users (AAAI 2022 · similarity 0.88)
- Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement (ICML 2023 · similarity 0.87)
- Regret in Online Recommendation Systems (NeurIPS 2020 · similarity 0.87)
- Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders (NeurIPS 2026 · similarity 0.85)
- Would You Like Your Data to Be Trained? A User Controllable Recommendation Framework (AAAI 2024 · similarity 0.85)
- Influential Exemplar Replay for Incremental Learning in Recommender Systems (AAAI 2024 · similarity 0.85)
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