Research map / Bandits and online learning / Multi-armed bandits
Position-based Multiple-play Bandit Problem with Unknown Position Bias
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PMED (permutation minimum empirical divergence) instead of MP-TS, which ignores position bias | multiple-play bandit with unknown position bias · Synthetic (5 arms, 2 slots) | regret at T=10^7: other algorithms suffer 100x or larger regret than LB | Helped |
| Use PMED instead of baselines (dcmKL-UCB, PBM-PIE, MP-TS) on real-world Tencent KDD Cup data | multiple-play bandit with unknown position bias · Real-world (Tencent KDD Cup 2012, 8 queries) | regret: gap not drastic compared with synthetic | Helped |
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