{"type":"ScholarlyArticle","title":"Towards Better Representation Learning for Personalized News Recommendation: a Multi-Channel Deep Fusion Approach","authors":[{"name":"Jianxun Lian","slug":"jianxun-lian","given":"Jianxun","family":"Lian","affiliation":"† University of Science and Technology of China , Hefei , China"},{"name":"Fuzheng Zhang","slug":"fuzheng-zhang","given":"Fuzheng","family":"Zhang","affiliation":"‡ Microsoft Research , Beijing , China"},{"name":"Xing Xie","slug":"xing-xie","given":"Xing","family":"Xie","affiliation":"‡ Microsoft Research , Beijing , China"},{"name":"Guangzhong Sun","slug":"guangzhong-sun","given":"Guangzhong","family":"Sun","affiliation":"† University of Science and Technology of China , Hefei , China"}],"abstract":"Millions of news articles emerge every day. How to provide personalized news recommendations has become a critical task for service providers. In the past few decades, latent factor models has been widely used for building recommender systems (RSs). With the remarkable success of deep learning techniques especially in visual computing and natural language understanding, more and more researchers have been trying to leverage deep neural networks to learn latent representations for advanced RSs. Following mainstream deep learning-based RSs, we propose a novel deep fusion model (DFM), which aims to improve the representation learning abilities in deep RSs and can be used for both candidate retrieval and item re-ranking. There are two key components in our DFM approach, namely an inception module and an attention mechanism. The inception module improves the plain multi-layer network via leveraging of various levels of interaction simultaneously, while the attention mechanism merges latent representations learnt from different channels in a customized fashion. We conduct extensive experiments on a commercial news reading dataset, and the results demonstrate that the proposed DFM is superior to several state-of-the-art models.","doi":"10.24963/ijcai.2018/529","url":"https://ijcai.org/proceedings/2018/529","pdf":"https://ijcai.org/proceedings/2018/0529.pdf","markdown":"https://ijcai.org/proceedings/2018/529.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/529","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":"Multidisciplinary Topics and Applications","track":"Main track","number":529,"pages":{"first":3805,"last":3811},"keywords":["Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning: Recommender Systems","Machine Learning: Neural Networks"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2012] Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kai- long Chen, Zhao Zheng, and Yong Yu. 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