{"type":"ScholarlyArticle","title":"Learning Concise Representations of Users' Influences through Online Behaviors","authors":[{"name":"Shenghua Liu","slug":"shenghua-liu","given":"Shenghua","family":"Liu","affiliation":"CAS Key Laboratory of Network Data Science & Technology; Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Houdong Zheng","slug":"houdong-zheng","given":"Houdong","family":"Zheng","affiliation":"School of Mathematics and Computer Science , Fuzhou University"},{"name":"Huawei Shen","slug":"huawei-shen","given":"Huawei","family":"Shen","affiliation":"CAS Key Laboratory of Network Data Science & Technology; Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Xueqi Cheng","slug":"xueqi-cheng","given":"Xueqi","family":"Cheng","affiliation":"CAS Key Laboratory of Network Data Science & Technology; Institute of Computing Technology , Chinese Academy of Sciences"},{"name":"Xiangwen Liao","slug":"xiangwen-liao","given":"Xiangwen","family":"Liao","affiliation":"School of Mathematics and Computer Science , Fuzhou University"}],"abstract":"Whereas it is well known that social network users influence each other, a fundamental problem in influence maximization, opinion formation and viral marketing is that users' influences are difficult to quantify. Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. However, such models do not consider how influences depend on each other if they originate from the same user or if they act on the same user. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. Given these problems, another way of defining the parameters is needed to consider the dependencies. Thus we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector. Such low-dimensional and distributed representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model's complexity. Additionally, the model can easily consider the sentimental polarities of users' messages and how sentiment affects users' influences. In this study, we conduct extensive experiments on real Microblog data, showing that our model with distributed representations achieves better accuracy than the state-of-the-art and pair-wise models, and that learning influences on sentiments benefit performance.","doi":"10.24963/ijcai.2017/327","url":"https://ijcai.org/proceedings/2017/327","pdf":"https://ijcai.org/proceedings/2017/0327.pdf","markdown":"https://ijcai.org/proceedings/2017/327.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/327","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":"Machine Learning A-R","track":"Main track","number":327,"pages":{"first":2351,"last":2357},"keywords":["Machine Learning: Data Mining","Machine Learning: Machine Learning","Multidisciplinary Topics and Applications: AI and Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Althoff et al., 2016] Tim Althoff, Pranav Jindal, and Jure Leskovec. 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