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Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>\n                    Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today\u2019s cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item metadata, and knowledge graphs will likely result in a poorly performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges\u2014handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity\u2014we designed a new end-to-end deep\n                    <jats:italic>A<\/jats:italic>\n                    dversarial\n                    <jats:italic>M<\/jats:italic>\n                    ulti-channel\n                    <jats:italic>T<\/jats:italic>\n                    ransfer network for\n                    <jats:italic>C<\/jats:italic>\n                    ross-\n                    <jats:italic>D<\/jats:italic>\n                    omain\n                    <jats:italic>R<\/jats:italic>\n                    ecommendation named\n                    <jats:italic>AMT-CDR<\/jats:italic>\n                    . Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs\u2014we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. Data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/bjtu-lucas-nlp\/AMT-CDR\">https:\/\/github.com\/bjtu-lucas-nlp\/AMT-CDR<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3641286","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T07:49:01Z","timestamp":1706341741000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["AMT-CDR: A Deep Adversarial Multi-Channel Transfer Network for Cross-Domain Recommendation"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4979-5097","authenticated-orcid":false,"given":"Kezhi","family":"Lu","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9977-8418","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0750-3693","authenticated-orcid":false,"given":"Danny","family":"Hughes","sequence":"additional","affiliation":[{"name":"KU Leuven, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3960-0583","authenticated-orcid":false,"given":"Guangquan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0690-4732","authenticated-orcid":false,"given":"Jie","family":"Lu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Layer normalization","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. 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