如何选择最好的广告集：我们能做得比贪心算法更好吗？
How to select the best set of ads: Can we do better than Greedy Algorithm?
摘要：
对于给定的一组关键字，选择最佳的广告集对广告商来说至关重要，这涉及到数百万候选人的广告组合。虽然点击率（CTR）很重要，但不同广告之间可能存在高度相关性，因此具有最高CTR的广告集不一定会使点击次数最大化。贪心算法是一种标准的、直接的方法来寻找一个足够体面的解决方案，但它不能保证是全局最优的。事实上，在我们所有的模拟中，超过70%的时间它都不是全局最优值，这意味着它很可能被困在局部最优值。本文提出了一种寻找最佳创意集的贪心-幂算法，即从传统的贪心算法的解开始，在其上进行另一个贪心算法搜索，可以选择几轮甚至无限轮。保证贪婪-权力算法不会更糟，因为它只会朝着增加目标函数的方向移动。我们证明了贪婪-权力算法的性能是一致的，并得出结论，它能够比贪心算法更好地执行系统。
英文摘要：
Selecting the best set of ads is critical for advertisers for a given set of keywords, which involves the composition of ads from millions of candidates. While click through rates (CTRs) are important, there could be high correlation among different ads, therefore the set of ads with top CTRs does not necessarily maximize the number of clicks. Greedy algorithm has been a standard and straightforward way to find out a decent enough solution, however, it is not guaranteed to be the global optimum. In fact, it proves not to be the global optimum more than 70% of the time across all our simulations, implying that it's very likely to be trapped at a local optimum. In this paper, we propose a Greedy-Power Algorithm to find out the best set of creatives, that is starting with the solution from the conventional Greedy Algorithm, one can perform another Greedy Algorithm search on top of it, with the option of a few or even infinite rounds. The Greedy-Power algorithm is guaranteed to be not worse, as it only moves in the direction to increase the goal function. We show that Greedy-Power Algorithm's performance is consistently better, and reach the conclusion that it is able to perform better than the Greedy Algorithm systematically.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
