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Previous research has established the crucial significance of mining contextual features and implicit relationships to enhance recommendation performance and alleviate data sparsity issues. However, the noise inherent in contextual features exacerbates data sparsity and hampers the ability of previous methods to explore implicit relationships for mitigating data sparsity. To address this challenge, we propose a variational type graph autoencoder model that attenuates the influence of noise in different types of context features by introducing type-specific latent variables. First, we introduce a heterogeneous denoising convolution module composed of two components: (1) Denoising attention aggregation is proposed to mitigate the influence of noisy structures and uncover implicit relationships. (2) A heterogeneous normalization module leverages context features within the same type to alleviate the effects of noise in context features and data sparsity. Furthermore, we propose a learnable heterogeneous mixture prior that assists in assigning different priors to distinct types of latent variables, effectively modeling different types of contextual features. Through comprehensive experiments conducted on real-world datasets, we demonstrate the compelling performance of our model compared to state-of-the-art competitive approaches.<\/jats:p>","DOI":"10.1145\/3703156","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T17:43:58Z","timestamp":1730828638000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Variational Type Graph Autoencoder for Denoising on Event Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4723-2154","authenticated-orcid":false,"given":"Shuo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2180-5986","authenticated-orcid":false,"given":"Xiangwu","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1805-8342","authenticated-orcid":false,"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,21]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"610","volume-title":"Proceedings of the 35th International Conference on Machine Learning (PMLR \u201918)","volume":"80","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski, Oleksandr Shchur, Daniel Z\u00fcgner, and Stephan G\u00fcnnemann. 2018. 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