Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Online Learning to Rank with Features
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RecurRank instead of CascadeLinUCB | Online ranking on the cascade click model · Cascade click model (CM) | regret: 0.53 vs 0.17 | Hurt |
| Use RecurRank instead of CascadeLinUCB | Online ranking under position-based click probabilities · Position-based click model (PBM) | regret: 227,736 -> 68,943 | Helped |
| Use RecurRank instead of TopRank | Online ranking under position-based click probabilities · Position-based click model (PBM) | regret: 745,177 -> 68,943 | Helped |
| Use RecurRank instead of CascadeLinUCB | MovieLens online ranking · MovieLens (ML) | regret: 180,256 -> 42,157 | Helped |
| Use RecurRank instead of TopRank | MovieLens online ranking · MovieLens (ML) | regret: 114,288 -> 42,157 | Helped |
| Use RecurRank instead of CascadeLinUCB | Online ranking on the cascade click model · Cascade click model (CM) | running time (s): 411 -> 51 | Helped |
| Use RecurRank instead of TopRank | Online ranking on the cascade click model · Cascade click model (CM) | running time (s): 176,772 -> 51 | Helped |
| Use RecurRank instead of CascadeLinUCB | Online ranking under position-based click probabilities · Position-based click model (PBM) | running time (s): 4,147 -> 310 | Helped |
| Use RecurRank instead of TopRank | Online ranking under position-based click probabilities · Position-based click model (PBM) | running time (s): 367,509 -> 310 | Helped |
| Use RecurRank instead of CascadeLinUCB | MovieLens online ranking · MovieLens (ML) | running time (s): 916 -> 234 | Helped |
| Use RecurRank instead of TopRank | MovieLens online ranking · MovieLens (ML) | running time (s): 4,868 -> 234 | Helped |
Closest papers by meaning
- TopRank: A practical algorithm for online stochastic ranking (NeurIPS 2018 · similarity 0.88)
- Online Learning to Rank in Stochastic Click Models (ICML 2017 · similarity 0.88)
- Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent (NeurIPS 2020 · similarity 0.86)
- Adaptively Learning to Select-Rank in Online Platforms (ICML 2024 · similarity 0.85)
- Sampling for Beyond-Worst-Case Online Ranking (AAAI 2024 · similarity 0.83)
- From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking (AAAI 2025 · similarity 0.82)
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