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Selecting the best set of ads is critical for advertisers for a given set ofkeywords, which involves the composition of ads from millions of candidates.While click through rates (CTRs) are important, there could be high correlationamong different ads, therefore the set of ads with top CTRs does notnecessarily maximize the number of clicks. Greedy algorithm has been a standardand straightforward way to find out a decent enough solution, however, it isnot guaranteed to be the global optimum. In fact, it proves not to be theglobal optimum more than 70% of the time across all our simulations, implyingthat it's very likely to be trapped at a local optimum. In this paper, wepropose a Greedy-Power Algorithm to find out the best set of creatives, that isstarting with the solution from the conventional Greedy Algorithm, one canperform another Greedy Algorithm search on top of it, with the option of a fewor even infinite rounds. The Greedy-Power algorithm is guaranteed to be notworse, as it only moves in the direction to increase the goal function. We showthat Greedy-Power Algorithm's performance is consistently better, and reach theconclusion that it is able to perform better than the Greedy Algorithmsystematically.

How to select the best set of ads: Can we do better than Greedy Algorithm?