search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and propose a new 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. 1
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