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Inf. Syst."],"published-print":{"date-parts":[[2023,4,30]]},"abstract":"<jats:p>This article presents a novel model named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users\u2019 positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1145\/3544105","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T12:24:09Z","timestamp":1655123049000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3565-6635","authenticated-orcid":false,"given":"Hanrui","family":"Wu","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6150-987X","authenticated-orcid":false,"given":"Jinyi","family":"Long","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Guangdong Key Lab of Traditional Chinese Medicine Information Technology, Jinan University, Pazhou Lab, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4566-7445","authenticated-orcid":false,"given":"Nuosi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Jinan University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1427-8807","authenticated-orcid":false,"given":"Dahai","family":"Yu","sequence":"additional","affiliation":[{"name":"TCL Corporate Research Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6833-5227","authenticated-orcid":false,"given":"Michael K.","family":"Ng","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Department of Mathematics, The University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3430028"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3010215"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482493"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467110"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/2043932.2044016"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2927224"},{"issue":"2","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3372154","article-title":"Local variational feature-based similarity models for recommending top-N new items","volume":"38","author":"Chen Yifan","year":"2020","unstructured":"Yifan Chen, Yang Wang, Xiang Zhao, Hongzhi Yin, Ilya Markov, and Maarten De Rijke. 2020. 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