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However, their performances drop significantly when the dataset is sparse. Most of the recent works failed to fully address this shortcoming. At most, some of them only tried to alleviate the problem by considering either user side or item side content information. In this article, we propose a novel recommender model called Hybrid Variational Autoencoder (HVAE) to improve the performance on sparse datasets. Different from the existing approaches, we encode both user and item information into a latent space for semantic relevance measurement. In parallel, we utilize collaborative filtering to find the implicit factors of users and items, and combine their outputs to deliver a hybrid solution. In addition, we compare the performance of Gaussian distribution and multinomial distribution in learning the representations of the textual data. Our experiment results show that HVAE is able to significantly outperform state-of-the-art models with robust performance.<\/jats:p>","DOI":"10.1145\/3470659","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T04:05:52Z","timestamp":1630728352000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Hybrid Variational Autoencoder for Recommender Systems"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9539-9530","authenticated-orcid":false,"given":"Hangbin","family":"Zhang","sequence":"first","affiliation":[{"name":"University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9814-6029","authenticated-orcid":false,"given":"Raymond K.","family":"Wong","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor W.","family":"Chu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Nanyang Avenue, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271696"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2210\u20132216","author":"Cao Ziqiang","year":"2015","unstructured":"Ziqiang Cao , Sujian Li , Yang Liu , Wenjie Li , and Heng Ji . 2015 . A novel neural topic model and its supervised extension . In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2210\u20132216 . Ziqiang Cao, Sujian Li, Yang Liu, Wenjie Li, and Heng Ji. 2015. A novel neural topic model and its supervised extension. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2210\u20132216."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.25","author":"Card Dallas","year":"2017","unstructured":"Dallas Card , Chenhao Tan , and Noah A Smith . 2017 . A neural framework for generalized topic models . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.25 . Dallas Card, Chenhao Tan, and Noah A Smith. 2017. A neural framework for generalized topic models. 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