{"type":"ScholarlyArticle","title":"Matrix completion with Preference Ranking for Top-N Recommendation","authors":[{"name":"Zengmao Wang","slug":"zengmao-wang","given":"Zengmao","family":"Wang","affiliation":"School of Computer Science , Wuhan University , Wuhan 430072 , China; School of Computer Science , Carleton University , Ottawa , Canada"},{"name":"Yuhong Guo","slug":"yuhong-guo","given":"Yuhong","family":"Guo","affiliation":"School of Computer Science , Carleton University , Ottawa , Canada"},{"name":"Bo Du","slug":"bo-du","given":"Bo","family":"Du","affiliation":"School of Computer Science , Wuhan University , Wuhan 430072 , China"}],"abstract":"Matrix completion has become a popular method for top-N recommendation due to the low rank nature of sparse rating matrices. However, many existing methods produce top-N recommendations by recovering a user-item matrix solely based on a low rank function or its relaxations, while ignoring other important intrinsic characteristics of the top-N recommendation tasks such as preference ranking over the items. In this paper, we propose a novel matrix completion method that integrates the low rank and preference ranking characteristics of recommendation matrix under a self-recovery model for top-N recommendation. The proposed method is formulated as a joint minimization problem and solved using an ADMM algorithm. We conduct experiments on E-commerce datasets. The experimental results show the proposed approach outperforms several state-of-the-art methods.","doi":"10.24963/ijcai.2018/498","url":"https://ijcai.org/proceedings/2018/498","pdf":"https://ijcai.org/proceedings/2018/0498.pdf","markdown":"https://ijcai.org/proceedings/2018/498.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/498","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":498,"pages":{"first":3585,"last":3591},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Cheng et al., 2014] Yao Cheng, Liang Yin, and Yong Yu. 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