Sponsored search advertising is a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers＇ bidding. This paper studi...Sponsored search advertising is a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers＇ bidding. This paper studies the optimal reserve price for a generalized second-price auction （GSP） under both static and dynamic settings. We show that if advertisers＇ per-click value has an increasing generalized failure rate, the search engine＇s revenue rate is quasi-concave and hence there exists an optimal reserve price under both settings. Different from a static GSP auction where the optimal reserve price is proved to be constant, in a dynamic setting the optimal reserve price is dependent on not only advertisers＇ per-click values, but also the number of ad links sold. A search engine should gradually raise reserve price as more qualified advertisers arrive, and maintain the same threshold after all first-page positions are occupied.展开更多
Advertisement （ad） selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad...Advertisement （ad） selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components （e.g., the auction mechanism） to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue （we call the combination of these objective functions as the marketplace objective for ease of reference）. To this end, we 1） extract a bunch of features to represent each pair of query and keyword, and 2） train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace objective. This formalization seems quite natural; however, it is technically difficult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.展开更多
文摘Sponsored search advertising is a significant revenue source for search engines. To ameliorate revenues, search engines often set fixed or variable reserve price to in influence advertisers＇ bidding. This paper studies the optimal reserve price for a generalized second-price auction （GSP） under both static and dynamic settings. We show that if advertisers＇ per-click value has an increasing generalized failure rate, the search engine＇s revenue rate is quasi-concave and hence there exists an optimal reserve price under both settings. Different from a static GSP auction where the optimal reserve price is proved to be constant, in a dynamic setting the optimal reserve price is dependent on not only advertisers＇ per-click values, but also the number of ad links sold. A search engine should gradually raise reserve price as more qualified advertisers arrive, and maintain the same threshold after all first-page positions are occupied.
文摘Advertisement （ad） selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components （e.g., the auction mechanism） to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue （we call the combination of these objective functions as the marketplace objective for ease of reference）. To this end, we 1） extract a bunch of features to represent each pair of query and keyword, and 2） train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace objective. This formalization seems quite natural; however, it is technically difficult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.
