{"type":"ScholarlyArticle","title":"Convolutional Gaussian Embeddings for Personalized Recommendation with Uncertainty","authors":[{"name":"Junyang Jiang","slug":"junyang-jiang","given":"Junyang","family":"Jiang","affiliation":"School of Computer Science , Shanghai Key Laboratory of Data Science , Fudan University , China"},{"name":"Deqing Yang","slug":"deqing-yang","given":"Deqing","family":"Yang","affiliation":"School of Data Science , Fudan University , China"},{"name":"Yanghua Xiao","slug":"yanghua-xiao","given":"Yanghua","family":"Xiao","affiliation":"School of Computer Science , Shanghai Key Laboratory of Data Science , Fudan University , China; Shanghai Institute of Intelligent Electronics & Systems , China"},{"name":"Chenlu Shen","slug":"chenlu-shen","given":"Chenlu","family":"Shen","affiliation":"School of Data Science , Fudan University , China"}],"abstract":"Most of existing embedding based recommendation models use embeddings (vectors) to represent users and items which contain latent features of users and items. Each of such embeddings corresponds to a single fixed point in low-dimensional space, thus fails to precisely represent the users/items with uncertainty which are often observed in recommender systems. Addressing this problem, we propose a unified deep recommendation framework employing Gaussian embeddings, which are proven adaptive to uncertain preferences exhibited by some users, resulting in better user representations and recommendation performance. Furthermore, our framework adopts Monte-Carlo sampling and convolutional neural networks to compute the correlation between the objective user and the candidate item, based on which precise recommendations are achieved. Our extensive experiments on two benchmark datasets not only justify that our proposed Gaussian embeddings capture the uncertainty of users very well, but also demonstrate its superior performance over the state-of-the-art recommendation models.","doi":"10.24963/ijcai.2019/367","url":"https://ijcai.org/proceedings/2019/367","pdf":"https://ijcai.org/proceedings/2019/0367.pdf","markdown":"https://ijcai.org/proceedings/2019/367.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/367","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 A-L","track":"Main track","number":367,"pages":{"first":2642,"last":2648},"keywords":["Machine Learning: Deep Learning","Machine Learning: Data Mining","Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Uncertainty in AI: Uncertainty Representations"],"videos":[],"references":[{"position":1,"raw":"[Bojchevski and Günnemann, 2018] Aleksandar Bojchevski and Stephan Günnemann. 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