{"type":"ScholarlyArticle","title":"Tag-Aware Personalized Recommendation Using a Hybrid Deep Model","authors":[{"name":"Zhenghua Xu","slug":"zhenghua-xu","given":"Zhenghua","family":"Xu","affiliation":"Department of Computer Science , University of Oxford , United Kingdom"},{"name":"Thomas Lukasiewicz","slug":"thomas-lukasiewicz","given":"Thomas","family":"Lukasiewicz","affiliation":"Department of Computer Science , University of Oxford , United Kingdom"},{"name":"Cheng Chen","slug":"cheng-chen","given":"Cheng","family":"Chen","affiliation":"School of Computer Science , Beijing University of Posts and Telecommunications , China"},{"name":"Yishu Miao","slug":"yishu-miao","given":"Yishu","family":"Miao","affiliation":"Department of Computer Science , University of Oxford , United Kingdom"},{"name":"Xiangwu Meng","slug":"xiangwu-meng","given":"Xiangwu","family":"Meng","affiliation":"School of Computer Science , Beijing University of Posts and Telecommunications , China"}],"abstract":"Recently, many efforts have been put into tag-aware personalized recommendation. However, due to uncontrolled vocabularies, social tags are usually redundant, sparse, and ambiguous. In this paper, we propose a deep neural network approach to solve this problem by mapping the tag-based user and item profiles to an abstract deep feature space, where the deep-semantic similarities between users and their target items (resp., irrelevant items) are maximized (resp., minimized). To ensure the scalability in practice, we further propose to improve this model's training efficiency by using hybrid deep learning and negative sampling. Experimental results show that our approach can significantly outperform the state-of-the-art baselines in tag-aware personalized recommendation (3.8 times better than the best baseline), and that using hybrid deep learning and negative sampling can dramatically enhance the model's training efficiency (hundreds of times quicker), while maintaining similar (and sometimes even better) training quality and recommendation performance.","doi":"10.24963/ijcai.2017/446","url":"https://ijcai.org/proceedings/2017/446","pdf":"https://ijcai.org/proceedings/2017/0446.pdf","markdown":"https://ijcai.org/proceedings/2017/446.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/446","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":446,"pages":{"first":3196,"last":3202},"keywords":["Multidisciplinary Topics and Applications: Personalization and User Modeling","Machine Learning: Learning Preferences or Rankings","Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2007] Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle. 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