{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:45:04Z","timestamp":1763729104589,"version":"3.45.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Recomm. Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to effectively address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items. Additionally, a user-level relevance scenario is considered, where the two related domains contain common users. In light of these scenarios, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims at simultaneously learning a pair of autoencoders in order to reveal the intrinsic representations in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learned to map the latent factors across domains. Extensive numerical experiments are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.<\/jats:p>\n                  <jats:p\/>","DOI":"10.1145\/3765614","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T11:39:57Z","timestamp":1756726797000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A cross-domain recommender system using deep coupled autoencoders"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5339-2018","authenticated-orcid":false,"given":"Alexandros","family":"Gkillas","sequence":"first","affiliation":[{"name":"Computer Engineering & Informatics, University of Patras - Patras Campus Rion","place":["Patra, Greece"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-1247","authenticated-orcid":false,"given":"Dimitrios","family":"Kosmopoulos","sequence":"additional","affiliation":[{"name":"Computer Engineering & Informatics, University of Patras - Patras Campus Rion","place":["Patra, Greece"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020504"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","unstructured":"J. Bobadilla F. Ortega A. Hernando and A. Guti\u00e9Rrez. 2013. Recommender systems survey. Know.-Based Syst. 46 (July 2013) 109\u2013132. DOI:10.1016\/j.knosys.2013.03.012","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_3_2_4_2","first-page":"3371","volume-title":"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol","author":"Ca Pascal Vincent@umontreal","year":"2010","unstructured":"Pascal Vincent@umontreal Ca, Larocheh@cs Toronto Edu, Isabelle Lajoie, Yoshua Bengio@umontreal Ca, and Pierre-Antoine Manzagol@umontreal Ca. 2010. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol. Technical Report. 3371\u20133408 pages."},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/978-1-4899-7637-6_27","volume-title":"Proceedings of the Recommender Systems Handbook, Second Edition","author":"Cantador Iv\u00e1n","year":"2015","unstructured":"Iv\u00e1n Cantador, Ignacio Fern\u00e1ndez-Tob\u00edas, Shlomo Berkovsky, and Paolo Cremonesi. 2015. Cross-domain recommender systems. In Proceedings of the Recommender Systems Handbook, Second Edition. Springer US, 919\u2013959."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3017429"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531967"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_9_2","first-page":"94","volume-title":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence","author":"Fu Wenjing","year":"2019","unstructured":"Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. 2019. Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence. AAAI Press, 94\u2013101."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313538"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","unstructured":"Ming He Jiuling Zhang and Shaozong Zhang. 2019. ACTL: Adaptive codebook transfer learning for cross-domain recommendation. IEEE Access 7 (2019) 19539\u201319549. DOI:10.1109\/ACCESS.2019.2896881","DOI":"10.1109\/ACCESS.2019.2896881"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271684"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.102"},{"key":"e_1_3_2_14_2","first-page":"379","volume-title":"Proceedings of the 18th International Conference on Artificial Intelligence and Statistics","author":"Iwata Tomoharu","year":"2015","unstructured":"Tomoharu Iwata and Takeuchi Koh. 2015. Cross-domain recommendation without shared users or items by sharing latent vector distributions. In Proceedings of the 18th International Conference on Artificial Intelligence and Statistics. PMLR, 379\u2013387."},{"key":"e_1_3_2_15_2","first-page":"20","volume-title":"Proceedings of the Advances in Information Retrieval.","author":"Kanagawa Heishiro","year":"2019","unstructured":"Heishiro Kanagawa, Hayato Kobayashi, Nobuyuki Shimizu, Yukihiro Tagami, and Taiji Suzuki. 2019. Cross-domain recommendation via deep domain adaptation. In Proceedings of the Advances in Information Retrieval.Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, and Djoerd Hiemstra (Eds.), Springer International Publishing, Cham, 20\u201329."},{"key":"e_1_3_2_16_2","unstructured":"Oleksii Kuchaiev and Boris Ginsburg. 2017. Training deep AutoEncoders for collaborative filtering. arXiv:1708.01715. Retrieved from https:\/\/arxiv.org\/abs\/1708.01715"},{"key":"e_1_3_2_17_2","first-page":"2052","volume-title":"Proceedings of the 21st International Joint Conference on Artificial Intelligence","author":"Li Bin","year":"2009","unstructured":"Bin Li, Qiang Yang, and Xiangyang Xue. 2009. Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (Pasadena, California, USA). 2052\u20132057."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548776"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371793"},{"issue":"1","key":"e_1_3_2_20_2","first-page":"321","article-title":"Dual metric learning for effective and efficient cross-domain recommendations","volume":"35","author":"Li Pan","year":"2021","unstructured":"Pan Li and Alexander Tuzhilin. 2021. Dual metric learning for effective and efficient cross-domain recommendations. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 321\u2013334.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331191"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380036"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems.","author":"Liu Weiming","year":"2021","unstructured":"Weiming Liu, Jiajie Su, Chaochao Chen, and Xiaolin Zheng. 2021. Leveraging distribution alignment via stein path for cross-domain cold-start recommendation. In Proceedings of the Advances in Neural Information Processing Systems.A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (Eds.), Retrieved from https:\/\/openreview.net\/forum?id=dYGFRxCf7P"},{"key":"e_1_3_2_24_2","first-page":"19223","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","volume":"34","author":"Liu Weiming","year":"2021","unstructured":"Weiming Liu, Jiajie Su, Chaochao Chen, and Xiaolin Zheng. 2021. Leveraging distribution alignment via stein path for cross-domain cold-start recommendation. In Proceedings of the Advances in Neural Information Processing Systems. M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (Eds.), Vol. 34, Curran Associates, Inc., 19223\u201319234. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/a0443c8c8c3372d662e9173c18faaa2c-Paper.pdf"},{"key":"e_1_3_2_25_2","first-page":"656","volume-title":"Proceedings of the Advances in Information Retrieval.","author":"Loni Babak","year":"2014","unstructured":"Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In Proceedings of the Advances in Information Retrieval.Maarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, and Katja Hofmann (Eds.), Springer International Publishing, Cham, 656\u2013661."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","unstructured":"Guofang Ma Yuexuan Wang Xiaolin Zheng Xiaoye Miao and Qianqiao Liang. 2021. A trust-aware latent space mapping approach for cross-domain recommendation. Neurocomputing 431 (2021) 100\u2013110. DOI:10.1016\/j.neucom.2020.12.015","DOI":"10.1016\/j.neucom.2020.12.015"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/S41019-019-00101-4"},{"key":"e_1_3_2_28_2","first-page":"2464","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Man Tong","year":"2017","unstructured":"Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach. In Proceedings of the International Joint Conference on Artificial Intelligence. 2464\u20132470."},{"key":"e_1_3_2_29_2","first-page":"443","volume-title":"Proceedings of the Advances in Information Retrieval.","author":"Manotumruksa Jarana","year":"2019","unstructured":"Jarana Manotumruksa, Dimitrios Rafailidis, Craig Macdonald, and Iadh Ounis. 2019. On cross-domain transfer in venue recommendation. In Proceedings of the Advances in Information Retrieval.Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, and Djoerd Hiemstra (Eds.), Springer International Publishing, Cham, 443\u2013456."},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1961209.1961213"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","unstructured":"Senthilselvan Natarajan Subramaniyaswamy Vairavasundaram Sivaramakrishnan Natarajan and Amir H. Gandomi. 2020. Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Systems with Applications 149 (2020) 113248. DOI:10.1016\/j.eswa.2020.113248","DOI":"10.1016\/j.eswa.2020.113248"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_33_2","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Pan Weike","year":"2010","unstructured":"Weike Pan, Evan W Xiang, Nathan N Liu, and Qiang Yang. 2010. Transfer learning in collaborative filtering for sparsity reduction. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.02.038"},{"key":"e_1_3_2_35_2","first-page":"1218","volume-title":"Proceedings of the 2018 IEEE International Conference on Data Mining","author":"Shang Jin","year":"2018","unstructured":"Jin Shang, Mingxuan Sun, and Kevyn Collins-Thompson. 2018. Demographic inference via knowledge transfer in cross-domain recommender systems. In Proceedings of the 2018 IEEE International Conference on Data Mining. 1218\u20131223. DOI:10.1109\/ICDM.2018.00162"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1145\/1401890.1401969","volume-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Singh Ajit P.","year":"2008","unstructured":"Ajit P. Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 650\u2013658."},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339730"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020480"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2907430"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2841924"},{"key":"e_1_3_2_41_2","first-page":"1827","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Xin Xin","year":"2015","unstructured":"Xin Xin, Zhirun Liu, Chin-Yew Lin, Heyan Huang, Xiaochi Wei, and Ping Guo. 2015. Cross-domain collaborative filtering with review text. In Proceedings of the International Joint Conference on Artificial Intelligence. 1827\u20131834."},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/587"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548455"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","unstructured":"Q. Zhang W. Liao G. Zhang B. Yuan and J. Lu. 2021. A deep dual adversarial network for cross-domain recommendation. In IEEE Transactions on Knowledge and Data Engineering 35 4 (2021) 3266\u20133278. DOI:10.1109\/TKDE.2021.3132953","DOI":"10.1109\/TKDE.2021.3132953"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2875144"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3576925"},{"key":"e_1_3_2_47_2","first-page":"2358","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence","author":"Zhang Zihan","year":"2016","unstructured":"Zihan Zhang, Xiaoming Jin, Lianghao Li, Guiguang Ding, and Qiang Yang. 2016. Multi-domain active learning for recommendation. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (Phoenix, Arizona). AAAI Press, 2358\u20132364."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10791-020-09378-W"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1145\/3357384.3358166","volume-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","author":"Zhao Cheng","year":"2019","unstructured":"Cheng Zhao, Chenliang Li, and Cong Fu. 2019. Cross-domain recommendation via preference propagation GraphNet. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (Beijing, China). Association for Computing Machinery, New York, NY, USA, 2165\u20132168. DOI:10.1145\/3357384.3358166"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","unstructured":"S.-T. Zhong L. Huang C.-D. Wang J.-H. Lai and P. S. Yu. 2020. An autoencoder framework with attention mechanism for cross-domain recommendation. In IEEE Transactions on Cybernetics 52 6 (2020) 5229\u20135241. DOI:10.1109\/TCYB.2020.3029002","DOI":"10.1109\/TCYB.2020.3029002"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357992"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/415"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","unstructured":"Feng Zhu Yan Wang Chaochao Chen Jun Zhou Longfei Li and Guanfeng Liu. 2021. Cross-domain recommendation: Challenges progress and prospects. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence IJCAI-21. 4721\u20134728. DOI:10.24963\/ijcai.2021\/639","DOI":"10.24963\/ijcai.2021\/639"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3104873"},{"key":"e_1_3_2_55_2","first-page":"1507","volume-title":"Proceedings of the 15th ACM International Conference on Web Search and Data Mining","author":"Zhu Yongchun","year":"2022","unstructured":"Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. 2022. Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 1507\u20131515."}],"container-title":["ACM Transactions on Recommender Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3765614","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:32:49Z","timestamp":1763728369000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3765614"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6,30]]}},"alternative-id":["10.1145\/3765614"],"URL":"https:\/\/doi.org\/10.1145\/3765614","relation":{},"ISSN":["2770-6699"],"issn-type":[{"type":"electronic","value":"2770-6699"}],"subject":[],"published":{"date-parts":[[2025,11,21]]},"assertion":[{"value":"2023-05-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}