{"type":"ScholarlyArticle","title":"Semi-supervised User Profiling with Heterogeneous Graph Attention Networks","authors":[{"name":"Weijian Chen","slug":"weijian-chen","given":"Weijian","family":"Chen","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Yulong Gu","slug":"yulong-gu","given":"Yulong","family":"Gu","affiliation":"JD.com , China"},{"name":"Zhaochun Ren","slug":"zhaochun-ren","given":"Zhaochun","family":"Ren","affiliation":"Shandong University , China"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Hongtao Xie","slug":"hongtao-xie","given":"Hongtao","family":"Xie","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Tong Guo","slug":"tong-guo","given":"Tong","family":"Guo","affiliation":"University of Science and Technology of China , Hefei , China"},{"name":"Dawei Yin","slug":"dawei-yin","given":"Dawei","family":"Yin","affiliation":"JD.com , China"},{"name":"Yongdong Zhang","slug":"yongdong-zhang","given":"Yongdong","family":"Zhang","affiliation":"University of Science and Technology of China , Hefei , China"}],"abstract":"Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance.  Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. \r\nThrough such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.","doi":"10.24963/ijcai.2019/293","url":"https://ijcai.org/proceedings/2019/293","pdf":"https://ijcai.org/proceedings/2019/0293.pdf","markdown":"https://ijcai.org/proceedings/2019/293.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/293","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":293,"pages":{"first":2116,"last":2122},"keywords":["Machine Learning: Classification","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Bojanowski et al., 2017] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 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