Research map / Bandits and online learning / Contextual bandits and Thompson sampling
UniRank: Unimodal Bandit Algorithms for Online Ranking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use UniRank instead of TopRank | Online ranking · Yandex query 8107157, Simul PBM, and Simul CM | cumulative regret | Helped |
| Use UniRank instead of TopRank | Online ranking · Other tested models | cumulative regret | Mixed |
| Use UniRank rather than a method requiring a known horizon | Online ranking · Tested click-model settings | cumulative regret | Helped |
| Use CascadeKL-UCB rather than generic algorithms | Online ranking · Cascading model | cumulative regret | Helped |
| Use PB-MHB rather than generic algorithms | Online ranking · Position Based Model | cumulative regret | Helped |
| Use GRAB on Yandex query 8107157 or Simul PBM | Online ranking · Yandex query 8107157 and Simul PBM | cumulative regret | Hurt |
| Use PB-MHB instead of UniRank | Online ranking · Yandex top 10 queries, CM, L=10 | computation time per recommendation: 1.0\pm0.2 -> 13.9 \pm 4.9 | Hurt |
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