{"type":"ScholarlyArticle","title":"VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders","authors":[{"name":"Xianwen Yu","slug":"xianwen-yu","given":"Xianwen","family":"Yu","affiliation":"Department of Software Engineering and Data Technology , Peking University"},{"name":"Xiaoning Zhang","slug":"xiaoning-zhang","given":"Xiaoning","family":"Zhang","affiliation":"SenseTime Group Limited"},{"name":"Yang Cao","slug":"yang-cao","given":"Yang","family":"Cao","affiliation":"SenseTime Group Limited"},{"name":"Min Xia","slug":"min-xia","given":"Min","family":"Xia","affiliation":"Department of Software Engineering and Data Technology , Peking University"}],"abstract":"Recently, Variational Autoencoders (VAEs) have been successfully applied to collaborative filtering for implicit feedback. However, the performance of the resulting model depends a lot on the expressiveness of the inference model and the latent representation is often too constrained to be expressive enough to capture the true posterior distribution. In this paper, a novel framework named VAEGAN is proposed to address the above issue. In VAEGAN, we first introduce Adversarial Variational Bayes (AVB) to train Variational Autoencoders with arbitrarily expressive inference model. By utilizing Generative Adversarial Networks (GANs) for implicit variational inference, the inference model provides better approximation to the posterior and maximum-likelihood assignment. Then the performance of our model is further improved by introducing an auxiliary discriminative network using adversarial training to achieve high accuracy in recommendation. Furthermore, contractive loss is added to the classical reconstruction cost function as a penalty term to yield robust features and improve the generalization performance. Finally, we show that the performance of our proposed VAEGAN significantly outperforms state-of-the-art baselines on several real-world datasets.","doi":"10.24963/ijcai.2019/584","url":"https://ijcai.org/proceedings/2019/584","pdf":"https://ijcai.org/proceedings/2019/0584.pdf","markdown":"https://ijcai.org/proceedings/2019/584.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/584","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 M-Z","track":"Main track","number":584,"pages":{"first":4206,"last":4212},"keywords":["Machine Learning: Adversarial Machine Learning","Multidisciplinary Topics and Applications: Recommender Systems","Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Bowman et al., 2015] Samuel R Bowman, Luke Vilnis, O- riol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. 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