Research map / Bandits and online learning / Contextual bandits and Thompson sampling
TopRank: A practical algorithm for online stochastic ranking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TopRank instead of BatchRank | Online ranking on the Yandex query simulations · Cascade model | regret: about three times lower | Helped |
| Use CascadeKL-UCB instead of TopRank | Online ranking on the Yandex query simulations · Cascade model | regret: about three times lower | Helped |
| Use TopRank instead of BatchRank | Online ranking on the Yandex query simulations · Position-based model | regret: about 30% lower | Helped |
| Use TopRank instead of CascadeKL-UCB | Online ranking on the Yandex query simulations · Position-based model, after 4 million steps | regret: after 4 million steps | Helped |
| Use CascadeKL-UCB instead of TopRank | Online ranking on the Yandex query simulations · Cascade model | regret | Helped |
| Use TopRank instead of CascadeKL-UCB | Online ranking on the Yandex query simulations · Position-based model, across queries | regret: about two thirds of queries | Mixed |
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