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Inf. Syst."],"published-print":{"date-parts":[[2022,1,31]]},"abstract":"<jats:p>\n            As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, autoencoder, and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a\n            <jats:underline>C<\/jats:underline>\n            ollaborative\n            <jats:underline>R<\/jats:underline>\n            eflection-Augmented\n            <jats:underline>A<\/jats:underline>\n            utoencoder\n            <jats:underline>N<\/jats:underline>\n            etwork (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user\u2019s pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANetmodel. We finally experimentally validate CRANeton four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https:\/\/github.com\/akaxlh\/CRANet.\n          <\/jats:p>","DOI":"10.1145\/3467023","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T15:31:23Z","timestamp":1631115083000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems"],"prefix":"10.1145","volume":"40","author":[{"given":"Lianghao","family":"Xia","sequence":"first","affiliation":[{"name":"South China University of Technology, Guangzhou, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Pokfulam, Hong Kong"}]},{"given":"Yong","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, Guangdong, China"}]},{"given":"Huance","family":"Xu","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, Guangdong, China"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Australian National University, Canberra, ACT, Australia"}]},{"given":"Weiguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"South China University of Technology, Guangzhou, Guangdong, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv:1706.02263. Retrieved from https:\/\/arxiv.org\/abs\/1706.02263.  Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv:1706.02263. Retrieved from https:\/\/arxiv.org\/abs\/1706.02263."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401042"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3281659"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3411754","article-title":"Block-aware item similarity models for Top-N recommendation","volume":"38","author":"Chen Yifan","year":"2020","unstructured":"Yifan Chen , Yang Wang , Xiang Zhao , Jie Zou , and Maarten De Rijke . 2020 . Block-aware item similarity models for Top-N recommendation . Transactions on Information Systems 38 , 4 (2020), 1 \u2013 26 . Yifan Chen, Yang Wang, Xiang Zhao, Jie Zou, and Maarten De Rijke. 2020. Block-aware item similarity models for Top-N recommendation. Transactions on Information Systems 38, 4 (2020), 1\u201326.","journal-title":"Transactions on Information Systems"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_4"},{"key":"e_1_2_1_6_1","volume-title":"International Conferences on Artificial Intelligence.","author":"Du Chao","year":"2018","unstructured":"Chao Du , Chongxuan Li , Yin Zheng , Jun Zhu , and Bo Zhang . 2018 . Collaborative filtering with user-item co-autoregressive models . In International Conferences on Artificial Intelligence. Chao Du, Chongxuan Li, Yin Zheng, Jun Zhu, and Bo Zhang. 2018. Collaborative filtering with user-item co-autoregressive models. In International Conferences on Artificial Intelligence."},{"key":"e_1_2_1_7_1","volume-title":"et\u00a0al","author":"Ekstrand Michael D.","year":"2011","unstructured":"Michael D. Ekstrand , John T. Riedl , Joseph A. Konstan , et\u00a0al . 2011 . 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