{"type":"ScholarlyArticle","title":"Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation","authors":[{"name":"Shengze Yu","slug":"shengze-yu","given":"Shengze","family":"Yu","affiliation":"Department of Computer Science and Technology , Tsinghua University , China"},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":"Department of Computer Science and Technology , Tsinghua University , China"},{"name":"Wenwu Zhu","slug":"wenwu-zhu","given":"Wenwu","family":"Zhu","affiliation":"Department of Computer Science and Technology , Tsinghua University , China"},{"name":"Peng Cui","slug":"peng-cui","given":"Peng","family":"Cui","affiliation":"Department of Computer Science and Technology , Tsinghua University , China"},{"name":"Jingdong Wang","slug":"jingdong-wang","given":"Jingdong","family":"Wang","affiliation":"Microsoft Research"}],"abstract":"Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned. However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities. In this paper, we propose a cross-platform association model for cross-platform video recommendation, i.e., Disparity-preserved Deep Cross-platform Association (DCA), taking platform-specific disparity and granularity difference into consideration. The proposed DCA model employs a partially-connected multi-modal autoencoder, which is capable of explicitly capturing platform-specific information, as well as utilizing nonlinear mapping functions to handle granularity differences. We then present a cross-platform video recommendation approach based on the proposed DCA model. Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.","doi":"10.24963/ijcai.2019/644","url":"https://ijcai.org/proceedings/2019/644","pdf":"https://ijcai.org/proceedings/2019/0644.pdf","markdown":"https://ijcai.org/proceedings/2019/644.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/644","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 Applications","track":"Main track","number":644,"pages":{"first":4635,"last":4641},"keywords":["Machine Learning Applications: Other Applications","Multidisciplinary Topics and Applications: Recommender Systems"],"videos":[],"references":[{"position":1,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. 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