{"type":"ScholarlyArticle","title":"Privileged Matrix Factorization for Collaborative Filtering","authors":[{"name":"Yali Du","slug":"yali-du","given":"Yali","family":"Du","affiliation":"† Center for Artificial Intelligence , FEIT , University of Technology Sydney"},{"name":"Chang Xu","slug":"chang-xu","given":"Chang","family":"Xu","affiliation":"‡ UBTech Sydney AI Institute , The School of IT , FEIT , The University of Sydney"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"‡ UBTech Sydney AI Institute , The School of IT , FEIT , The University of Sydney"}],"abstract":"Collaborative filtering plays a crucial role in reducing excessive information in online consuming by suggesting products to customers that fulfil their potential interests. Observing that a user's review comments on purchases are often in companion with ratings, recent works exploit the review texts in representing user or item factors and have achieved prominent performance. Although effectiveness of reviews has been verified, one major defect of existing works is that reviews are used in justifying the learning of either user or item factors without noticing that each review associates a pair of user and item concurrently. To better explore the value of review comments, this paper presents the privileged matrix factorization method that utilize reviews in the learning of both user and item factors. By mapping review texts into the privileged feature space, a learned privileged function compensates the discrepancies between predicted ratings and groundtruth values rating-wisely. Thus by minimizing discrepancies and prediction errors, our method harnesses the information present in the review comments for the learning of both user and item factors. Experiments on five real datasets testify the effectiveness of the proposed method.","doi":"10.24963/ijcai.2017/223","url":"https://ijcai.org/proceedings/2017/223","pdf":"https://ijcai.org/proceedings/2017/0223.pdf","markdown":"https://ijcai.org/proceedings/2017/223.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/223","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":223,"pages":{"first":1610,"last":1616},"keywords":["Machine Learning: Machine Learning","Machine Learning: Feature Selection/Construction","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Agarwal and Chen, 2009] Deepak Agarwal and Bee-Chung Chen. 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