{"type":"ScholarlyArticle","title":"Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective","authors":[{"name":"Qi Liu","slug":"qi-liu","given":"Qi","family":"Liu","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , Universitys of S&T of China"},{"name":"Guifeng Wang","slug":"guifeng-wang","given":"Guifeng","family":"Wang","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , Universitys of S&T of China"},{"name":"Hongke Zhao","slug":"hongke-zhao","given":"Hongke","family":"Zhao","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , Universitys of S&T of China"},{"name":"Chuanren Liu","slug":"chuanren-liu","given":"Chuanren","family":"Liu","affiliation":"‡ Decision Sciences & MIS Department , Drexel University"},{"name":"Tong Xu","slug":"tong-xu","given":"Tong","family":"Xu","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , Universitys of S&T of China"},{"name":"Enhong Chen","slug":"enhong-chen","given":"Enhong","family":"Chen","affiliation":"† Anhui Province Key Lab. of Big Data Analysis and Application , Universitys of S&T of China"}],"abstract":"Crowdfunding is an emerging Internet application for creators designing campaigns (projects) to collect funds from public investors. Usually, the limited budget of the creator is manually divided into several perks (reward options), that should fit various market demand and further bring different monetary contributions for the campaign. Therefore, it is very challenging for each creator to design an effective campaign. To this end, in this paper, we aim to enhance the funding performance of the newly proposed campaigns, with a focus on optimizing the product supply of perks. Specifically, given the expected budget and the perks of a campaign, we propose a novel solution to automatically recommend the optimal product supply to every perk for balancing the expected return of this campaign against the risk. Along this line, we define it as a constrained portfolio selection problem, where the risk of each campaign is measured by a multi-task learning method.  Finally, experimental results on the real-world crowdfunding data clearly prove that the optimized product supply can help improve the campaign performance significantly, and meanwhile, our multi-task learning method could more precisely estimate the risk of each campaign.","doi":"10.24963/ijcai.2017/97","url":"https://ijcai.org/proceedings/2017/97","pdf":"https://ijcai.org/proceedings/2017/0097.pdf","markdown":"https://ijcai.org/proceedings/2017/97.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/97","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Constraints and Satisfiability","track":"Main track","number":97,"pages":{"first":695,"last":702},"keywords":["Constraints and Satisfiability: Applications","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Barnett, 2015] Chance Barnett. 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