{"type":"ScholarlyArticle","title":"Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks","authors":[{"name":"Xiaotian Han","slug":"xiaotian-han","given":"Xiaotian","family":"Han","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Chuan Shi","slug":"chuan-shi","given":"Chuan","family":"Shi","affiliation":"Beijing University of Posts and Telecommunications"},{"name":"Senzhang Wang","slug":"senzhang-wang","given":"Senzhang","family":"Wang","affiliation":"Nanjing University of Aeronautics and Astronautics"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"University of Illinois at Chicago; Institute for Data Science , Tsinghua University"},{"name":"Li Song","slug":"li-song","given":"Li","family":"Song","affiliation":"Beijing University of Posts and Telecommunications"}],"abstract":"Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the rating information between users and items, although some recently extended models add some auxiliary information to learn a unified latent factor between users and items.  The unified latent factor only represents the latent features of users and items from the aspect of purchase history. However, the latent features of users and items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling rich objects and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items through different meta-paths and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are effectively fused with an attention mechanism for the top-N recommendation. Extensive experiments on three real datasets show that NeuACF significantly outperforms both existing latent factor models and recent neural network models.","doi":"10.24963/ijcai.2018/471","url":"https://ijcai.org/proceedings/2018/471","pdf":"https://ijcai.org/proceedings/2018/0471.pdf","markdown":"https://ijcai.org/proceedings/2018/471.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/471","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":471,"pages":{"first":3393,"last":3399},"keywords":["Machine Learning Applications: Networks","Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 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