{"type":"ScholarlyArticle","title":"Recommendation with Multi-Source Heterogeneous Information","authors":[{"name":"Li Gao","slug":"li-gao","given":"Li","family":"Gao","affiliation":"* Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; ‡ JD.com , Beijing , China School of Cyber Security ,"},{"name":"Hong Yang","slug":"hong-yang","given":"Hong","family":"Yang","affiliation":"† Centre for Artificial Intelligence , University of Technology Sydney , Australia Department of Computing ,"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"University of Chinese Academy of Sciences , Beijing , China"},{"name":"Chuan Zhou","slug":"chuan-zhou","given":"Chuan","family":"Zhou","affiliation":"* Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; ‡ JD.com , Beijing , China School of Cyber Security ,"},{"name":"Weixue Lu","slug":"weixue-lu","given":"Weixue","family":"Lu","affiliation":"§ Macquarie University , Sydney , Australia Data Science Lab ,"},{"name":"Yue Hu","slug":"yue-hu","given":"Yue","family":"Hu","affiliation":"* Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; ‡ JD.com , Beijing , China School of Cyber Security ,"}],"abstract":"Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.","doi":"10.24963/ijcai.2018/469","url":"https://ijcai.org/proceedings/2018/469","pdf":"https://ijcai.org/proceedings/2018/0469.pdf","markdown":"https://ijcai.org/proceedings/2018/469.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/469","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":469,"pages":{"first":3378,"last":3384},"keywords":["Machine Learning: Machine Learning","Machine Learning: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2015] Shiyu Chang, Wei Han, Jiliang Tang, Guo Jun Qi, Charu C. 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