{"type":"ScholarlyArticle","title":"Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation","authors":[{"name":"Yanan Xu","slug":"yanan-xu","given":"Yanan","family":"Xu","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Yanmin Zhu","slug":"yanmin-zhu","given":"Yanmin","family":"Zhu","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Yanyan Shen","slug":"yanyan-shen","given":"Yanyan","family":"Shen","affiliation":"Shanghai Jiao Tong University , Shanghai , China"},{"name":"Jiadi Yu","slug":"jiadi-yu","given":"Jiadi","family":"Yu","affiliation":"Shanghai Jiao Tong University , Shanghai , China"}],"abstract":"Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.","doi":"10.24963/ijcai.2019/642","url":"https://ijcai.org/proceedings/2019/642","pdf":"https://ijcai.org/proceedings/2019/0642.pdf","markdown":"https://ijcai.org/proceedings/2019/642.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/642","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 Applications","track":"Main track","number":642,"pages":{"first":4620,"last":4626},"keywords":["Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning Applications: Applications of Supervised Learning","Multidisciplinary Topics and Applications: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Atwood and Towsley, 2016] James Atwood and Don Towsley. 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