{"type":"ScholarlyArticle","title":"Success Prediction on Crowdfunding with Multimodal Deep Learning","authors":[{"name":"Chaoran Cheng","slug":"chaoran-cheng","given":"Chaoran","family":"Cheng","affiliation":"Department of Computer Science , New Jersey Institute of Technology , USA"},{"name":"Fei Tan","slug":"fei-tan","given":"Fei","family":"Tan","affiliation":"Department of Computer Science , New Jersey Institute of Technology , USA"},{"name":"Xiurui Hou","slug":"xiurui-hou","given":"Xiurui","family":"Hou","affiliation":"Department of Computer Science , New Jersey Institute of Technology , USA"},{"name":"Zhi Wei","slug":"zhi-wei","given":"Zhi","family":"Wei","affiliation":"Department of Computer Science , New Jersey Institute of Technology , USA"}],"abstract":"We consider the problem of project success prediction on crowdfunding platforms. Despite the information in a project profile can be of different modalities such as text, images, and metadata, most existing prediction approaches leverage only the text dominated modality. Nowadays rich visual images have been utilized in more and more project profiles for attracting backers, little work has been conducted to evaluate their effects towards success prediction. Moreover, meta information has been exploited in many existing approaches for improving prediction accuracy. However, such meta information is usually limited to the dynamics after projects are posted, e.g., funding dynamics such as comments and updates. Such a requirement of using after-posting information makes both project creators and platforms not able to predict the outcome in a timely manner. In this work, we designed and evaluated advanced neural network schemes that combine information from different modalities to study the influence of sophisticated interactions among textual, visual, and metadata on project success prediction. To make pre-posting prediction possible, our approach requires only information collected  from the pre-posting profile. Our extensive experimental results show that the image features could improve success prediction performance significantly, particularly for project profiles with little text information. Furthermore, we identified contributing elements.","doi":"10.24963/ijcai.2019/299","url":"https://ijcai.org/proceedings/2019/299","pdf":"https://ijcai.org/proceedings/2019/0299.pdf","markdown":"https://ijcai.org/proceedings/2019/299.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/299","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":"Machine Learning A-L","track":"Main track","number":299,"pages":{"first":2158,"last":2164},"keywords":["Machine Learning: Classification","Computer Vision: Language and Vision","Machine Learning: Deep Learning","Machine Learning: Data Mining","Machine Learning Applications: Other Applications"],"videos":[],"references":[{"position":1,"raw":"[Csurka et al., 2004] Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray. 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