{"type":"ScholarlyArticle","title":"Jointly Learning Prices and Product Features","authors":[{"name":"Ehsan Emamjomeh-Zadeh","slug":"ehsan-emamjomeh-zadeh","given":"Ehsan","family":"Emamjomeh-Zadeh","affiliation":""},{"name":"Renato Paes Leme","slug":"renato-paes-leme","given":"Renato","family":"Paes Leme","affiliation":""},{"name":"Jon Schneider","slug":"jon-schneider","given":"Jon","family":"Schneider","affiliation":""},{"name":"Balasubramanian Sivan","slug":"balasubramanian-sivan","given":"Balasubramanian","family":"Sivan","affiliation":""}],"abstract":"Product Design is an important problem in marketing research where a firm tries\r\nto learn what features of a product are more valuable to consumers. We study\r\nthis problem from the viewpoint of online learning: a firm repeatedly interacts\r\nwith a buyer by choosing a product configuration as well as a price and\r\nobserving the buyer's purchasing decision. The goal of the firm is to maximize\r\nrevenue throughout the course of $T$ rounds by\r\nlearning the buyer's preferences.\r\n\r\nWe study both the case of a set of discrete products and the case of a continuous set of\r\nallowable product features. In both cases we provide nearly tight\r\nupper and lower regret bounds.","doi":"10.24963/ijcai.2021/325","url":"https://ijcai.org/proceedings/2021/325","pdf":"https://ijcai.org/proceedings/2021/0325.pdf","markdown":"https://ijcai.org/proceedings/2021/325.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/325","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":325,"pages":{"first":2360,"last":2366},"keywords":["Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems","Machine Learning: Online Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}