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Yunhong Zhou


Publications by Yunhong Zhou (bibliography)

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Zhou, Yunhong and Naroditskiy, Victor (2008): Algorithm for stochastic multiple-choice knapsack problem and application to keywords bidding. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1175-1176. http://doi.acm.org/10.1145/1367497.1367713

We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and design an algorithm to solve S-MCKP and the corresponding bidding optimization problem. Our algorithm selects items online based on a threshold function which can be built/updated using historical data. Our algorithm achieved about 99% performance compared to the offline optimum when applied to a real bidding dataset. With synthetic dataset and iid item-sets, its performance ratio against the offline optimum converges to one empirically with increasing number of periods.

© All rights reserved Zhou and Naroditskiy and/or ACM Press

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Zhou, Yunhong, Chakrabarty, Deeparnab and Lukose, Rajan (2008): Budget constrained bidding in keyword auctions and online knapsack problems. In: Proceedings of the 2008 International Conference on the World Wide Web 2008. pp. 1243-1244. http://doi.acm.org/10.1145/1367497.1367747

We consider the budget-constrained bidding optimization problem for sponsored search auctions, and model it as an online (multiple-choice) knapsack problem. We design both deterministic and randomized algorithms for the online (multiple-choice) knapsack problems achieving a provably optimal competitive ratio. This translates back to fully automatic bidding strategies maximizing either profit or revenue for the budget-constrained advertiser. Our bidding strategy for revenue maximization is oblivious (i.e., without knowledge) of other bidders' prices and/or click-through-rates for those positions. We evaluate our bidding algorithms using both synthetic data and real bidding data gathered manually, and also discuss a sniping heuristic that strictly improves bidding performance. With sniping and parameter tuning enabled, our bidding algorithms can achieve a performance ratio above 90% against the optimum by the omniscient bidder.

© All rights reserved Zhou et al. and/or ACM Press

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Zhou, Yunhong and Lukose, Rajan M. (2007): Vindictive bidding in keyword auctions. In: Gini, Maria L., Kauffman, Robert J., Sarppo, Donna, Dellarocas, Chrysanthos and Dignum, Frank (eds.) Proceedings of the 9th International Conference on Electronic Commerce - ICEC 2007 August 19-22, 2007, Minneapolis, MN, USA. pp. 141-146. http://doi.acm.org/10.1145/1282100.1282130

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