{"type":"ScholarlyArticle","title":"Learning Discriminative Recommendation Systems with Side Information","authors":[{"name":"Feipeng Zhao","slug":"feipeng-zhao","given":"Feipeng","family":"Zhao","affiliation":"Computer and Information Sciences Temple University , Philadelphia , USA"},{"name":"Yuhong Guo","slug":"yuhong-guo","given":"Yuhong","family":"Guo","affiliation":"School of Computer Science Carleton University , Ottawa , Canada"}],"abstract":"Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.","doi":"10.24963/ijcai.2017/485","url":"https://ijcai.org/proceedings/2017/485","pdf":"https://ijcai.org/proceedings/2017/0485.pdf","markdown":"https://ijcai.org/proceedings/2017/485.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/485","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 S-Z","track":"Main track","number":485,"pages":{"first":3469,"last":3475},"keywords":["Machine Learning: Semi-Supervised Learning","Multidisciplinary Topics and Applications: Personalization and User Modeling"],"videos":[],"references":[{"position":1,"raw":"[Aiolli, 2014] Fabio Aiolli. 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