{"type":"ScholarlyArticle","title":"Preferred Deals in General Environments","authors":[{"name":"Yuan Deng","slug":"yuan-deng","given":"Yuan","family":"Deng","affiliation":"Duke University"},{"name":"Sébastien Lahaie","slug":"sebastien-lahaie","given":"Sébastien","family":"Lahaie","affiliation":"Google Research"},{"name":"Vahab Mirrokni","slug":"vahab-mirrokni","given":"Vahab","family":"Mirrokni","affiliation":"Google Research"}],"abstract":"A preferred deal is a special contract for selling impressions of display ad inventory. By accepting a deal, a buyer agrees to buy a minimum amount of impressions at a fixed price per impression, and is granted priority access to the impressions before they are sent to an open auction on an ad exchange. We consider the problem of designing preferred deals (inventory, price, quantity) in the presence of general convex constraints, including budget constraints, and propose an approximation algorithm to maximize the revenue obtained from the deals. We then evaluate our algorithm using auction data from a major advertising exchange and our empirical results show that the algorithm achieves around 95% of the optimal revenue.","doi":"10.24963/ijcai.2019/33","url":"https://ijcai.org/proceedings/2019/33","pdf":"https://ijcai.org/proceedings/2019/0033.pdf","markdown":"https://ijcai.org/proceedings/2019/33.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/33","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":33,"pages":{"first":231,"last":237},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Agent-based and Multi-agent Systems: Algorithmic Game Theory"],"videos":[],"references":[{"position":1,"raw":"[Azar et al., 2017] Yossi Azar, Michal Feldman, Nick Gravin, and Alan Roytman. 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