Global Optimization for Advertisement Selection in Sponsored Search

Advertisement (ad) selection plays an important role and will heavily influence the effectiveness of the subsequent methods regard ad selection as a relatively independent module, queries and keywords during the ad selection process. In this paper, Our proposal is to formulate ad selection as such an optimization downstream components (e.g., the auction mechanism) to achieve and search engine revenue (we call the combination of these objective reference). To this end, we 1) extract a bunch of features to machine learning model that maps the features to a binary variable maximizing the aforementioned marketplace objective. This formalization difficult because the marketplace objective is non-convex, discontinuous, due to the ranking and second-price rules in the auction mechanism. approximation of the marketplace objective, which is smooth and techniques. We test the ad selection model learned with our commercial search engine. The experimental results show that our algorithms on all the metrics under investigation.