{"type":"ScholarlyArticle","title":"Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation","authors":[{"name":"Zekai Wang","slug":"zekai-wang","given":"Zekai","family":"Wang","affiliation":"School of Software and Microelectronics , Peking University , Beijing , P.R. China"},{"name":"Hongzhi Liu","slug":"hongzhi-liu","given":"Hongzhi","family":"Liu","affiliation":"School of Software and Microelectronics , Peking University , Beijing , P.R. China"},{"name":"Yingpeng Du","slug":"yingpeng-du","given":"Yingpeng","family":"Du","affiliation":"School of Software and Microelectronics , Peking University , Beijing , P.R. China"},{"name":"Zhonghai Wu","slug":"zhonghai-wu","given":"Zhonghai","family":"Wu","affiliation":"National Engineering Center of Software Engineering , Peking University , Beijing , P.R. China; Key Lab of High Confidence Software Technologies (MOE) , Peking University , Beijing , P.R. China"},{"name":"Xing Zhang","slug":"xing-zhang","given":"Xing","family":"Zhang","affiliation":"School of Electronics Engineering and Computer Science , Peking University , Beijing , P.R. China"}],"abstract":"Most of heterogeneous information network (HIN) based recommendation models are based on the user and item modeling with meta-paths. However, they always model users and items in isolation under each meta-path, which may lead to information extraction misled. In addition, they only consider structural features of HINs when modeling users and items during exploring HINs, which may lead to useful information for recommendation lost irreversibly. To address these problems, we propose a HIN based unified embedding model for recommendation, called HueRec. We assume there exist some common characteristics under different meta-paths for each user or item, and use data from all meta-paths to learn unified users’ and items’ representations. So the interrelation between meta-paths are utilized to alleviate the problems of data sparsity and noises on one meta-path. Different from existing models which first explore HINs then make recommendations, we combine these two parts into an end-to-end model to avoid useful information lost in initial phases. In addition, we embed all users, items and meta-paths into related latent spaces. Therefore, we can measure users’ preferences on meta-paths to improve the performances of personalized recommendation. Extensive experiments show HueRec consistently outperforms state-of-the-art methods.","doi":"10.24963/ijcai.2019/529","url":"https://ijcai.org/proceedings/2019/529","pdf":"https://ijcai.org/proceedings/2019/0529.pdf","markdown":"https://ijcai.org/proceedings/2019/529.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/529","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 M-Z","track":"Main track","number":529,"pages":{"first":3813,"last":3819},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio. 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