The scientific program will consist of symposia and poster sessions. Topics related to theoretical and applied research in the domain of toxicology and toxicological studies on chemicals of public concern will be welc...The scientific program will consist of symposia and poster sessions. Topics related to theoretical and applied research in the domain of toxicology and toxicological studies on chemicals of public concern will be welcome. The presenter’s name, address, and telephone and FAX numbers should be submitted along with the title of the presentation and whether it is oral or poster. Deadline: April 15, 1990. Papers should be submitted to:展开更多
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
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
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
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
China＇s Ministry of Science and Technology recently announced approval of China＇s National Basic Research Program （the 973 Program） and the Major Research Plan projects in 2008. Five Tsinghua research projects wil...China＇s Ministry of Science and Technology recently announced approval of China＇s National Basic Research Program （the 973 Program） and the Major Research Plan projects in 2008. Five Tsinghua research projects will be sponsored by the 973 Program and two by the Major Research Plan. Seven professors were appointed as chief scientists for the projects. Tsinghua has undertaken 28 projects since the 973 Program＇s inception involving 28 chief scientists and eight Major Research Plan projects involving eight chief scientists. Tsinghua is one of the country＇s leading institutions for supervision and conduct of the 973 Program projects.展开更多
China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan U...China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan University Huazhong University of Science and Technology Wuhan Transportation University.展开更多
China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan U...China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan University Huazhong University of Science and Technology Wuhan Transportation University.展开更多
文摘The scientific program will consist of symposia and poster sessions. Topics related to theoretical and applied research in the domain of toxicology and toxicological studies on chemicals of public concern will be welcome. The presenter’s name, address, and telephone and FAX numbers should be submitted along with the title of the presentation and whether it is oral or poster. Deadline: April 15, 1990. Papers should be submitted to:
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.
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.
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.
文摘China＇s Ministry of Science and Technology recently announced approval of China＇s National Basic Research Program （the 973 Program） and the Major Research Plan projects in 2008. Five Tsinghua research projects will be sponsored by the 973 Program and two by the Major Research Plan. Seven professors were appointed as chief scientists for the projects. Tsinghua has undertaken 28 projects since the 973 Program＇s inception involving 28 chief scientists and eight Major Research Plan projects involving eight chief scientists. Tsinghua is one of the country＇s leading institutions for supervision and conduct of the 973 Program projects.
文摘China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan University Huazhong University of Science and Technology Wuhan Transportation University.
文摘China Ship Scientific Research Center Peking University Institute of Water Conservancy and Hydroelectric power Research Shanghai Jiaotong University Fudan University Zhejiang University Tsinghua University Zhongshan University Huazhong University of Science and Technology Wuhan Transportation University.
