The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past data.But since these data are provided by strategic buyers in the auctions,they can be ...The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past data.But since these data are provided by strategic buyers in the auctions,they can be manipulated,which may hurt the seller’s revenue.We model this problem as a Private Data Manipulation(PDM)game:the seller first announces an auction(such as Myerson’s)whose allocation and payment rules depend on the value distributions of buyers;the buyers then submit fake value distributions to the seller to implement the auction.The seller’s expected revenue and the buyers’expected utilities depend on the auction rule and the game played among the buyers in their choices of the submitted distributions.Under the PDM game,we show that Myerson’s auction is equivalent to the generalized first-price auction,and under further assumptions equivalent to the Vickrey-Clarke-Groves(VCG)auction and the generalized second-price auction.Our results partially explain why Myerson’s auction is not as popular as the generalized second-price auction in the practice of sponsored search auctions,and provide new perspectives into data-driven decision making in mechanism design.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
Sponsored search auction has been recently studied and auctioneer's revenue is an important consideration in probabilistic single-item second-price auctions. Some papers have analyzed the revenue maximization prob...Sponsored search auction has been recently studied and auctioneer's revenue is an important consideration in probabilistic single-item second-price auctions. Some papers have analyzed the revenue maximization problem on different methods to bundle contexts. In this paper, we propose a more flexible and natural method which is called the bracketing method. We prove that finding a bracketing scheme that maximizes the auctioneer's revenue is strongly NP-hard. Then, a heuristic algorithm is given. Experiments on three test cases show that the revenue of the optimal bracketing scheme is very close to the optimal revenue without any bundling constraint, and the heuristic algorithm performs very well. Finally, we consider a simpler model that for each row in the valuation matrix, the non-zero cells have the same value. We prove that the revenue maximization problem with Kanonymous signaling scheme and cardinality constrained signaling scheme in this simpler model are both NP-hard.展开更多
基金supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project(No.2018AAA0100901)National Natural Science Foundation of China(Nos.61761146005 and 61632017).
文摘The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past data.But since these data are provided by strategic buyers in the auctions,they can be manipulated,which may hurt the seller’s revenue.We model this problem as a Private Data Manipulation(PDM)game:the seller first announces an auction(such as Myerson’s)whose allocation and payment rules depend on the value distributions of buyers;the buyers then submit fake value distributions to the seller to implement the auction.The seller’s expected revenue and the buyers’expected utilities depend on the auction rule and the game played among the buyers in their choices of the submitted distributions.Under the PDM game,we show that Myerson’s auction is equivalent to the generalized first-price auction,and under further assumptions equivalent to the Vickrey-Clarke-Groves(VCG)auction and the generalized second-price auction.Our results partially explain why Myerson’s auction is not as popular as the generalized second-price auction in the practice of sponsored search auctions,and provide new perspectives into data-driven decision making in mechanism design.
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.
基金the National Natural Science Foundation of China (Grant No. 61672012).
文摘Sponsored search auction has been recently studied and auctioneer's revenue is an important consideration in probabilistic single-item second-price auctions. Some papers have analyzed the revenue maximization problem on different methods to bundle contexts. In this paper, we propose a more flexible and natural method which is called the bracketing method. We prove that finding a bracketing scheme that maximizes the auctioneer's revenue is strongly NP-hard. Then, a heuristic algorithm is given. Experiments on three test cases show that the revenue of the optimal bracketing scheme is very close to the optimal revenue without any bundling constraint, and the heuristic algorithm performs very well. Finally, we consider a simpler model that for each row in the valuation matrix, the non-zero cells have the same value. We prove that the revenue maximization problem with Kanonymous signaling scheme and cardinality constrained signaling scheme in this simpler model are both NP-hard.
