Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Interactive Submodular Bandit
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SM-UCB (UCB on marginal gains with greedy selection) instead of RANDOM baseline | interactive submodular bandit · MovieLens movie recommendation (single-function facility location) | accumulated objective value f(S) | Helped |
| Use SM-UCB instead of HISTORY-FREE, FEATURE-FREE, CONTEXT-FREE, and RANDOM baselines | interactive submodular bandit · MovieLens per-user recommendation (40 user groups) | collective objective value | Helped |
| Use SM-UCB instead of baselines | interactive submodular bandit · Interactive influence maximization (Facebook network) | fraction of influenced nodes | Helped |
| Use SM-UCB instead of baselines | interactive submodular bandit · Yahoo! news recommendation | collective objective value | Helped |
| Use SM-UCB instead of baselines | interactive submodular bandit · Personalized data summarization (Reuters) | average topic coverage | Helped |
| Remove the history of previously selected items (HISTORY-FREE ablation) (ablation) | SM-UCB · MovieLens per-user recommendation | collective objective value | Hurt |
| Remove the user context features (FEATURE-FREE ablation) (ablation) | SM-UCB · MovieLens per-user recommendation | collective objective value | Hurt |
| Remove both context and selection history (CONTEXT-FREE, i.e. GP-SELECT) (ablation) | SM-UCB · MovieLens per-user recommendation | collective objective value | Hurt |
Closest papers by meaning
- Probabilistic Submodular Maximization in Sub-Linear Time (ICML 2017 · similarity 0.86)
- Incentivized Exploration with Stochastic Covariates: A Two-Stage Mechanism Design for Recommender System (ICML 2026 · similarity 0.85)
- Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability (NeurIPS 2024 · similarity 0.85)
- Online Two-Stage Submodular Maximization (NeurIPS 2025 · similarity 0.85)
- Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback (NeurIPS 2017 · similarity 0.85)
- Leveraging Offline Data in Linear Latent Contextual Bandits (ICML 2025 · similarity 0.84)
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