{"type":"ScholarlyArticle","title":"Hybrid Item-Item Recommendation via Semi-Parametric Embedding","authors":[{"name":"Peng Hu","slug":"peng-hu","given":"Peng","family":"Hu","affiliation":"Alibaba Group , Hang Zhou , China"},{"name":"Rong Du","slug":"rong-du","given":"Rong","family":"Du","affiliation":"Alibaba Group , Hang Zhou , China"},{"name":"Yao Hu","slug":"yao-hu","given":"Yao","family":"Hu","affiliation":"Alibaba Group , Hang Zhou , China"},{"name":"Nan Li","slug":"nan-li","given":"Nan","family":"Li","affiliation":"Alibaba Group , Hang Zhou , China"}],"abstract":"Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i.e., the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile,  a deep learning algorithm is proposed to learn two parts simultaneously.  Extensive experiments on  real-world datasets demonstrate the effectiveness and robustness of the proposed method.","doi":"10.24963/ijcai.2019/350","url":"https://ijcai.org/proceedings/2019/350","pdf":"https://ijcai.org/proceedings/2019/0350.pdf","markdown":"https://ijcai.org/proceedings/2019/350.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/350","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":350,"pages":{"first":2521,"last":2527},"keywords":["Machine Learning: Ensemble Methods","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Agarwal et al., 2011] Deepak Agarwal, Bee-Chung Chen, and Bo Long. 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