Research map / Bandits and online learning / Online convex optimization and games
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the flow-based delivery policy instead of Random, HWM, Probability-Greedy, or Degree-Greedy | Guaranteed advertising delivery · Random demand-supply graphs; average degrees 5, 10, 15, 20, and 25 | competitive ratio: about 0.015 for Random-Normalization and 0.035 for Gauss-Perturbation at average degree 5, compared with Degree-Greedy | Helped |
| Increase average user-type degree | The studied delivery policies · Random demand-supply graphs | competitive ratio | Helped |
| Increase the number of exposures per ad campaign | The studied delivery policies · Random-Normalization and Gauss-Perturbation; average degrees 5 and 10 | competitive ratio | No clear effect |
| Use an estimated user-type distribution with bounded relative error | Flow-based delivery policy · CMPUT with distribution error bounded by δ | expected user traffic consumption: (1+O(n^{-ε/2}))(1+δ)/(1-δ) | Hurt |
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