{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:13:43Z","timestamp":1783541623165,"version":"3.55.0"},"publisher-location":"New York, NY","reference-count":87,"publisher":"Springer US","isbn-type":[{"value":"9781071621967","type":"print"},{"value":"9781071621974","type":"electronic"}],"license":[{"start":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T00:00:00Z","timestamp":1330041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2012,2,24]],"date-time":"2012-02-24T00:00:00Z","timestamp":1330041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-1-0716-2197-4_13","type":"book-chapter","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T21:04:15Z","timestamp":1650575055000},"page":"485-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Design and Evaluation of Cross-Domain Recommender Systems"],"prefix":"10.1007","author":[{"given":"Maurizio Ferrari","family":"Dacrema","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iv\u00e1n","family":"Cantador","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Fern\u00e1ndez-Tob\u00edas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shlomo","family":"Berkovsky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Cremonesi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2012,2,24]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"F. Abel, S. Ara\u00fajo, Q. Gao, G.-J. Houben, Analyzing cross-system user modeling on the social web, in 11th International Conference on Web Engineering, pp. 28\u201343 (2011)","DOI":"10.1007\/978-3-642-22233-7_3"},{"issue":"2-3","key":"13_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s11257-012-9131-2","volume":"23","author":"F Abel","year":"2013","unstructured":"F. Abel, E. Helder, G.-J. Houben, N. Henze, D. Krause, Cross-system user modeling and personalization on the social web. User Model. User Adap. Inter. 23(2-3), 169\u2013209 (2013)","journal-title":"User Model. User Adap. Inter."},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"S. Berkovsky, T. Kuflik, F. Ricci, Entertainment personalization mechanism through cross-domain user modeling, in 1st International Conference on Intelligent Technologies for Interactive Entertainment, pp. 215\u2013219 (2005)","DOI":"10.1007\/11590323_22"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"S. Berkovsky, T. Kuflik, F. Ricci, Cross-domain mediation in collaborative filtering, in 11th International Conference on User Modeling, pp. 355\u2013359 (2007)","DOI":"10.1007\/978-3-540-73078-1_44"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"S. Berkovsky, T. Kuflik, F. Ricci, Distributed collaborative filtering with domain specialization, in 1st ACM Conference on Recommender Systems, pp. 33\u201340 (2007)","DOI":"10.1145\/1297231.1297238"},{"issue":"3","key":"13_CR6","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s11257-007-9042-9","volume":"18","author":"S Berkovsky","year":"2008","unstructured":"S. Berkovsky, T. Kuflik, F. Ricci, Mediation of user models for enhanced personalization in recommender systems. User Model. User Adap. Inter. 18(3), 245\u2013286 (2008)","journal-title":"User Model. User Adap. Inter."},{"issue":"1","key":"13_CR7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s13735-012-0032-2","volume":"2","author":"M Braunhofer","year":"2013","unstructured":"M. Braunhofer, M. Kaminskas, F. Ricci, Location-aware music recommendation. Int. J. Multimedia Inf. Retr. 2(1), 31\u201344 (2013)","journal-title":"Int. J. Multimedia Inf. Retr."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"I. Cantador, I. Fern\u00e1ndez-Tob\u00edas, S. Berkovsky, P. Cremonesi, Cross-domain recommender systems. Recommender Systems Handbook, 2nd edn. (Springer, 2015), pp. 919\u2013959","DOI":"10.1007\/978-1-4899-7637-6_27"},{"issue":"1-2","key":"13_CR9","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ins.2008.08.022","volume":"179","author":"F Carmagnola","year":"2009","unstructured":"F. Carmagnola, F. Cena, User identification for cross-system personalisation. Information Sciences 179(1-2), 16\u201332 (2009)","journal-title":"Information Sciences"},{"issue":"3","key":"13_CR10","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11257-011-9097-5","volume":"21","author":"F Carmagnola","year":"2011","unstructured":"F. Carmagnola, F. Cena, C. Gena, User model interoperability: A survey. User Model. User Adap. Inter. 21(3), 285\u2013331 (2011)","journal-title":"User Model. User Adap. Inter."},{"key":"13_CR11","unstructured":"B. Cao, N.N. Liu, Q. Yang, Transfer learning for collective link prediction in multiple heterogeneous domains, in 27th International Conference on Machine Learning, pp. 159\u2013166 (2010)"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"R. Chung, D. Sundaram, A. Srinivasan, Integrated personal recommender systems, in 9th International Conference on Electronic Commerce, pp. 65\u201374 (2007)","DOI":"10.1145\/1282100.1282113"},{"key":"13_CR13","unstructured":"P.T. Costa, R.R. McCrae, Revised NEO personality inventory (NEO-PI-R) and NEO five-factor inventory (NEO-FFI) manual. Psychol. Assess. Resour. (1992)"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"P. Cremonesi, A. Tripodi, R. Turrin, Cross-domain recommender systems, in 11th IEEE International Conference on Data Mining Workshops, pp. 496\u2013503 (2011)","DOI":"10.1109\/ICDMW.2011.57"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"P. Cremonesi, M. Quadrana, Cross-domain recommendations without overlapping data: myth or reality? in 8th ACM Conference on Recommender Systems (2014)","DOI":"10.1145\/2645710.2645769"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"C. Ding, T. Li, W. Peng, H. Park, Orthogonal nonnegative matrix tri-factorizations for clustering, in 12th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 126\u2013135 (2006)","DOI":"10.1145\/1150402.1150420"},{"key":"13_CR17","unstructured":"R. Driskill, J. Riedl, Recommender systems for E-commerce: Challenges and opportunities, in AAAI\u201999 Workshop on Artificial Intelligence for Electronic Commerce, pp. 73\u201376 (1999)"},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"M. Enrich, M. Braunhofer, F. Ricci, Cold-start management with cross-domain collaborative filtering and tags, in 14th International Conference on E-Commerce and Web Technologies, pp. 101\u2013112 (2013)","DOI":"10.1007\/978-3-642-39878-0_10"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"M.D. Ekstrand, R. Burke, F. Diaz, Fairness and discrimination in recommendation and retrieval, in Proceedings of the 13th ACM Conference on Recommender Systems, pp. 576\u2013577 (2019)","DOI":"10.1145\/3298689.3346964"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"A. Farseev, I. Samborskii, A. Filchenkov, T. Chua, Cross-domain recommendation via clustering on multi-layer graphs, in 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 195\u2013204 (2017)","DOI":"10.1145\/3077136.3080774"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"I. Fern\u00e1ndez-Tob\u00edas, I. Cantador, M. Kaminskas, F. Ricci, A generic semantic-based framework for cross-domain recommendation, in 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 25\u201332 (2011)","DOI":"10.1145\/2039320.2039324"},{"key":"13_CR22","unstructured":"I. Fern\u00e1ndez-Tob\u00edas, I. Cantador, M. Kaminskas, F. Ricci, Cross-domain recommender systems: A survey of the state of the art, in 2nd Spanish Conference on Information Retrieval, pp. 187\u2013198 (2012)"},{"key":"13_CR23","unstructured":"I. Fern\u00e1ndez-Tob\u00edas, I. Cantador, Exploiting social tags in matrix factorization models for cross-domain collaborative filtering, in 1st International Workshop on New Trends in Content-based Recommender Systems (2013)"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"M. Ferrari Dacrema, S. Boglio, P. Cremonesi, D. Jannach, A troubling analysis of reproducibility and progress in recommender systems research. ACM Trans. Inf. Syst. 39(2) 49 p. (2021)","DOI":"10.1145\/3434185"},{"key":"13_CR25","doi-asserted-by":"crossref","unstructured":"M. Ferrari Dacrema, P. Cremonesi, D. Jannach, Are we really making much progress? A worrying analysis of recent neural recommendation approaches, in 13th ACM Conference on Recommender Systems, pp. 101\u2013109 (2019)","DOI":"10.1145\/3298689.3347058"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"M. Ferrari Dacrema, F. Parroni, P. Cremonesi, D. Jannach, Critically examining the claimed value of convolutions over user-item embedding maps for recommender systems, in 29th ACM International Conference on Information & Knowledge Management, pp. 355\u2013363 (2020)","DOI":"10.1145\/3340531.3411901"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"J. Freyne, S. Berkovsky, G. Smith, Evaluating recommender systems for supportive technologies, in User Modeling and Adaptation for Daily Routines, pp. 195\u2013217 (2013)","DOI":"10.1007\/978-1-4471-4778-7_8"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"W. Fu, Z. Peng, S. Wang, Y. Xu, J. Li, Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems, in 33rd AAAI Conference on Artificial Intelligence, pp. 94\u2013101 (2019)","DOI":"10.1609\/aaai.v33i01.330194"},{"issue":"1","key":"13_CR29","first-page":"1","volume":"3","author":"C Gao","year":"2019","unstructured":"C. Gao, C. Huang, Y. Yu, H. Wang, Y. Li, D. Jin, Privacy-preserving cross-domain location recommendation. Proc. ACM Interactive Mobile Wearable Ubiquit. Technol. 3(1), 1\u201321 (2019)","journal-title":"Proc. ACM Interactive Mobile Wearable Ubiquit. Technol."},{"key":"13_CR30","doi-asserted-by":"crossref","unstructured":"S. Gao, H. Luo, D. Chen, S. Li, P. Gallinari, J. Guo, Cross-domain recommendation via cluster-level latent factor model, in 17th and 24th European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 161\u2013176 (2013)","DOI":"10.1007\/978-3-642-40991-2_11"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"M. He, J. Zhang, P. Yang, K. Yao, Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation, in 11th International Conference on Web Search and Data Mining, WSDM, pp. 225\u2013233 (2018)","DOI":"10.1145\/3159652.3159675"},{"issue":"1","key":"13_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1145\/963770.963772","volume":"22","author":"JL Helocker","year":"2004","unstructured":"J.L. Helocker, J.A. Konstan, L.G. Terveen, J. Riedl, Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5\u201353 (2004)","journal-title":"ACM Trans. Inf. Syst."},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, C. Zhu, Personalized recommendation via cross-domain triadic factorization, in 22nd International Conference on World Wide Web, pp. 595\u2013606 (2013)","DOI":"10.1145\/2488388.2488441"},{"key":"13_CR34","doi-asserted-by":"publisher","first-page":"2822","DOI":"10.1145\/3308558.3313543","volume":"2019","author":"G Hu","year":"2019","unstructured":"G. Hu, Y. Zhang, Q. Yang, Transfer meets hybrid: A synthetic approach for cross-domain collaborative filtering with text, in The World Wide Web Conference 2019, pp. 2822\u20132829 (2019)","journal-title":"The World Wide Web Conference"},{"key":"13_CR35","unstructured":"D. Jannach, P. Cremonesi, M. Quadrana, Session-based recommender systems. Recommender Systems Handbook, 3nd edn. (Springer, 2021)"},{"issue":"10","key":"13_CR36","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"S Jialin Pan","year":"2010","unstructured":"S. Jialin Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"M. Kaminskas, I. Fern\u00e1ndez-Tob\u00edas, F. Ricci, I. Cantador, Ontology-based identification of music for places, in 13th International Conference on Information and Communication Technologies in Tourism, pp. 436\u2013447 (2013)","DOI":"10.1007\/978-3-642-36309-2_37"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"S. Kang, J. Hwang, D. Lee, H. Yu, Semi-supervised learning for cross-domain recommendation to cold-start users, in 28th ACM International Conference on Information and Knowledge Management, CIKM, pp. 1563\u20131572 (2019)","DOI":"10.1145\/3357384.3357914"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"M. Khan, R. Ibrahim, I. Ghani, Cross domain recommender systems: A systematic literature review. ACM Comput. Surv. 36:1\u201336:34 (2017)","DOI":"10.1145\/3073565"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"B. Kitts, D. Freed, M. Vrieze, Cross-sell: A fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities, in 6th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 437\u2013446 (2000)","DOI":"10.1145\/347090.347181"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Y. Koren, Factorization meets the neighborhood: A multifaceted collaborative filtering model, in 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"issue":"3","key":"13_CR42","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/S0957-4174(01)00034-3","volume":"21","author":"CH Lee","year":"2001","unstructured":"C.H. Lee, Y.H. Kim, P.K. Rhee, Web personalization expert with combining collaborative filtering and association rule mining technique. Expert Syst. Appl. 21(3), 131\u2013137 (2001)","journal-title":"Expert Syst. Appl."},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"B. Li, Cross-domain collaborative filtering: A brief survey, in 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 1085\u20131086 (2011)","DOI":"10.1109\/ICTAI.2011.184"},{"key":"13_CR44","unstructured":"B. Li, Q. Yang, X. Xue, Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction, in 21st International Joint Conference on Artificial Intelligence, pp. 2052\u20132057 (2009)"},{"key":"13_CR45","doi-asserted-by":"crossref","unstructured":"B. Li, Q. Yang, X. Xue, Transfer learning for collaborative filtering via a rating-matrix generative model, in 26th International Conference on Machine Learning, pp. 617\u2013624 (2009)","DOI":"10.1145\/1553374.1553454"},{"key":"13_CR46","unstructured":"B. Li, X. Zhu, R. Li, C. Zhang, X. Xue, X. Wu, Cross-domain collaborative filtering over time, in 22nd International Joint Conference on Artificial Intelligence, pp. 2293\u20132298 (2011)"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"P. Li, A. Tuzhilin, DDTCDR: Deep dual transfer cross domain recommendation, in 13th ACM International Conference on Web Search and Data Mining, pp. 331\u2013339 (2020)","DOI":"10.1145\/3336191.3371793"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"J. Lian, F. Zhang, X. Xie, G. Sun, CCCFNet: A content-boosted collaborative filtering neural network for cross domain recommender systems, in 26th International Conference on World Wide Web Companion, pp. 817\u2013818 (2017)","DOI":"10.1145\/3041021.3054207"},{"key":"13_CR49","doi-asserted-by":"crossref","unstructured":"B. Liu, Y. Wei, Y. Zhang, Z. Yan, Q. Yang, Transferable contextual bandit for cross-domain recommendation, in 32nd Conference on Artificial Intelligence (AAAI), pp. 3619\u20133626 (2018)","DOI":"10.1609\/aaai.v32i1.11699"},{"key":"13_CR50","first-page":"2768","volume":"2020","author":"J Liu","year":"2020","unstructured":"J. Liu, P. Zhao, F. Zhuang, Y. Liu, V. Sheng, J. Xu, X. Zhou, H. Xiong, Exploiting aesthetic preference in deep cross networks for cross-domain recommendation, in The Web Conference 2020, pp. 2768\u20132774 (2020)","journal-title":"The Web Conference"},{"key":"13_CR51","unstructured":"A. Loizou, How to recommend music to film buffs: enabling the provision of recommendations from multiple domains. Ph.D. thesis, University of Southampton (2009)"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"B. Loni, Y. Shi, M.A. Larson, A. Hanjalic, Cross-domain collaborative filtering with factorization machines, in 36th European Conference on Information Retrieval (2014)","DOI":"10.1007\/978-3-319-06028-6_72"},{"key":"13_CR53","doi-asserted-by":"crossref","unstructured":"Y. Low, D. Agarwal, A.J. Smola, Multiple domain user personalization, in 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 123\u2013131 (2011)","DOI":"10.1145\/2020408.2020434"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"M. Ludewig, N. Mauro, S. Latifi, D. Jannach, Empirical analysis of session-based recommendation algorithms. User Model. User Adap. Inter., 1\u201333 (2020)","DOI":"10.1007\/s11257-020-09277-1"},{"key":"13_CR55","doi-asserted-by":"crossref","unstructured":"M. Ma, P. Ren, Y. Lin, Z. Chen, J. Ma, M. De Rijke, \u03c0-Net: A parallel information-sharing network for shared-account cross-domain sequential recommendations, in 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 685\u2013694 (2019)","DOI":"10.1145\/3331184.3331200"},{"key":"13_CR56","doi-asserted-by":"crossref","unstructured":"T. Man, H. Shen, X. Jin, X. Cheng, Cross-domain recommendation: An embedding and mapping approach, in 26th International Joint Conference on Artificial Intelligence, IJCAI, pp. 2464\u20132470 (2017)","DOI":"10.24963\/ijcai.2017\/343"},{"key":"13_CR57","doi-asserted-by":"crossref","unstructured":"O. Moreno, B. Shapira, L. Rokach, G. Shani, TALMUD: transfer learning for multiple domains, in 21st ACM Conference on Information and Knowledge Management, pp. 425\u2013434 (2012)","DOI":"10.1145\/2396761.2396817"},{"key":"13_CR58","unstructured":"M. Nakatsuji, Y. Fujiwara, A. Tanaka, T. Uchiyama, T. Ishida, Recommendations over domain specific user graphs, in 19th European Conference on Artificial Intelligence, pp. 607\u2013612 (2010)"},{"key":"13_CR59","unstructured":"S.J. Pan, J.T. Kwok, Q. Yang, Transfer learning via dimensionality reduction, in 23rd AAAI Conference on Artificial Intelligence, pp. 677\u2013682 (2008)"},{"key":"13_CR60","doi-asserted-by":"crossref","unstructured":"R. Pagano, P. Cremonesi, M. Larson, B. Hidasi, D. Tikk, A. Karatzoglou, M. Quadrana, The contextual turn: From context-aware to context-driven recommender systems, in 10th ACM Conference on Recommender Systems, pp. 249\u2013252 (2016)","DOI":"10.1145\/2959100.2959136"},{"issue":"10","key":"13_CR61","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"13_CR62","unstructured":"W. Pan, N.N. Liu, E.W. Xiang, Q. Yang, Transfer learning to predict missing ratings via heterogeneous user feedbacks, in 22nd International Joint Conference on Artificial Intelligence, pp. 2318\u20132323 (2011)"},{"key":"13_CR63","doi-asserted-by":"crossref","unstructured":"W. Pan, E.W. Xiang, N.N. Liu, Q. Yang, Transfer learning in collaborative filtering for sparsity reduction, in 24th AAAI Conference on Artificial Intelligence, pp. 210\u2013235 (2010)","DOI":"10.1609\/aaai.v24i1.7578"},{"key":"13_CR64","doi-asserted-by":"crossref","unstructured":"W. Pan, E.W. Xiang, Q. Yang, Transfer learning in collaborative filtering with uncertain ratings, in 26th AAAI Conference on Artificial Intelligence, pp. 662\u2013668 (2012)","DOI":"10.1609\/aaai.v26i1.8197"},{"key":"13_CR65","unstructured":"Z. Ren, L. Zhao, J. Ma, M. de Rijke, Mixed information flow for cross-domain sequential recommendations. ACM Trans. Knowl. Discov. Data 1(1), (2020)"},{"issue":"3","key":"13_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2168752.2168771","volume":"3","author":"S Rendle","year":"2012","unstructured":"S. Rendle, Factorization machines with libFM. ACM Trans. Intell. Syst. Tech. 3(3), 1\u201322 (2012)","journal-title":"ACM Trans. Intell. Syst. Tech."},{"key":"13_CR67","doi-asserted-by":"crossref","unstructured":"S. Sahebi, P. Brusilovsky, Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation, in 21st International Conference on User Modeling, Adaptation, and Personalization, pp. 289\u2013295 (2013)","DOI":"10.1007\/978-3-642-38844-6_25"},{"key":"13_CR68","doi-asserted-by":"crossref","unstructured":"G. Shani, A. Gunawardana, Evaluating recommendation systems. Recommender Systems Handbook, pp. 257\u2013297 (2011)","DOI":"10.1007\/978-0-387-85820-3_8"},{"issue":"2-3","key":"13_CR69","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11257-012-9128-x","volume":"23","author":"B Shapira","year":"2013","unstructured":"B. Shapira, L. Rokach, S. Freilikhman, Facebook single and cross domain data for recommendation systems. User Model. User Adap. Inter. 23(2-3), 211\u2013247 (2013)","journal-title":"User Model. User Adap. Inter."},{"key":"13_CR70","doi-asserted-by":"crossref","unstructured":"Y. Shi, M. Larson, A. Hanjalic, Tags as bridges between domains: Improving recommendation with Tag-induced cross-domain collaborative filtering, in 19th International Conference on User Modeling, Adaption, and Personalization, pp. 305\u2013316 (2011)","DOI":"10.1007\/978-3-642-22362-4_26"},{"key":"13_CR71","doi-asserted-by":"crossref","unstructured":"A. Stewart, E. Diaz-Aviles, W. Nejdl, L.B. Marinho, A. Nanopoulos, L. Schmidt-Thieme, Cross-tagging for personalized open social networking, in 20th ACM Conference on Hypertext and Hypermedia, pp. 271\u2013278 (2009)","DOI":"10.1145\/1557914.1557960"},{"key":"13_CR72","doi-asserted-by":"crossref","unstructured":"A. Taneja, A. Arora, Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl., 304\u2013316 (2018)","DOI":"10.1016\/j.eswa.2017.09.042"},{"key":"13_CR73","doi-asserted-by":"crossref","unstructured":"J. Tang, J. Yan, L. Ji, M. Zhang, S. Guo, N. Liu, X. Wang, Z. Chen, Collaborative users\u2019 brand preference mining across multiple domains from implicit feedbacks, in 25th AAAI Conference on Artificial Intelligence, pp. 477\u2013482 (2011)","DOI":"10.1609\/aaai.v25i1.7899"},{"key":"13_CR74","doi-asserted-by":"crossref","unstructured":"A. Tiroshi, S. Berkovsky, M.A. Kaafar, T. Chen, T. Kuflik, Cross social networks interests predictions based on graph features, in 7th ACM Conference on Recommender Systems, pp. 319\u2013322 (2013)","DOI":"10.1145\/2507157.2507206"},{"key":"13_CR75","doi-asserted-by":"crossref","unstructured":"A. Tiroshi, T. Kuflik, Domain ranking for cross domain collaborative filtering, in 20th International Conference on User Modeling, Adaptation, and Personalization, pp. 328\u2013333 (2012)","DOI":"10.1007\/978-3-642-31454-4_30"},{"key":"13_CR76","doi-asserted-by":"crossref","unstructured":"D. Vallet, S. Berkovsky, S. Ardon, A. Mahanti, M.A. Kafaar, Characterizing and predicting viral-and-popular video content, in 24th ACM International on Conference on Information and Knowledge Management, pp. 1591\u20131600 (2015)","DOI":"10.1145\/2806416.2806556"},{"issue":"8","key":"13_CR77","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1109\/TNNLS.2019.2907430","volume":"31","author":"C Wang","year":"2020","unstructured":"C. Wang, M. Niepert, H. Li, RecSys-DAN: Discriminative adversarial networks for cross-domain recommender systems. IEEE Trans. Neural Netw. Learn. Syst. 31(8), 2731\u20132740 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"13_CR78","first-page":"209","volume":"26","author":"P Winoto","year":"2008","unstructured":"P. Winoto, T. Tang, If you like the devil wears prada the book, will you also enjoy the devil wears prada the movie? A study of cross-domain recommendations. N. Gener. Comput. 26, 209\u2013225 (2008)","journal-title":"A study of cross-domain recommendations. N. Gener. Comput."},{"key":"13_CR79","doi-asserted-by":"crossref","unstructured":"F. Yuan, L. Yao, B. Benatallah, DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns, in 28th International Joint Conference on Artificial Intelligence, pp. 4227\u20134233 (2019)","DOI":"10.24963\/ijcai.2019\/587"},{"key":"13_CR80","unstructured":"Y. Zhang, B. Cao, D.-Y. Yeung, Multi-domain collaborative filtering, in 26th Conference on Uncertainty in Artificial Intelligence, pp. 725\u2013732 (2010)"},{"key":"13_CR81","doi-asserted-by":"crossref","unstructured":"Y. Zang, X. Hu, LKT-FM: A novel rating pattern transfer model for improving non-overlapping cross-domain collaborative filtering, in Machine Learning and Knowledge Discovery in Databases - European Conference, pp. 641\u2013656 (2017)","DOI":"10.1007\/978-3-319-71246-8_39"},{"key":"13_CR82","doi-asserted-by":"crossref","unstructured":"X. Zhang, J. Cheng, T. Yuan, B. Niu, H. Lu, TopRec: domain-specific recommendation through community topic mining in social network, in 22nd International Conference on World Wide Web, pp. 1501\u20131510 (2013)","DOI":"10.1145\/2488388.2488519"},{"key":"13_CR83","doi-asserted-by":"crossref","unstructured":"L. Zhao, S.J. Pan, E.W. Xiang, E. Zhong, X. Lu, Q. Yang, Active transfer learning for cross-system recommendation, in 27th AAAI Conference on Artificial Intelligence, pp. 1205\u20131211 (2013)","DOI":"10.1609\/aaai.v27i1.8458"},{"key":"13_CR84","doi-asserted-by":"crossref","unstructured":"C. Zhao, C. Li, R. Xiao, H. Deng, A. Sun, CATN: Cross-domain recommendation for cold-start users via aspect transfer network, in 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp. 229\u2013238 (2020)","DOI":"10.1145\/3397271.3401169"},{"key":"13_CR85","doi-asserted-by":"crossref","unstructured":"F. Zhu, Y. Wang, C. Chen, G. Liu, M. Orgun, A deep framework for cross-domain and cross-system recommendations, in 27th International Joint Conference on Artificial Intelligence, IJCAI, pp. 3711\u20133717 (2018)","DOI":"10.24963\/ijcai.2018\/516"},{"key":"13_CR86","doi-asserted-by":"crossref","unstructured":"F. Zhu, C. Chen, Y. Wang, G. Liu, X. Zheng, DTCDR: A framework for dual-target cross-domain recommendation, in 28th ACM International Conference on Information and Knowledge Management, pp. 1533\u20131542 (2019)","DOI":"10.1145\/3357384.3357992"},{"issue":"12","key":"13_CR87","doi-asserted-by":"publisher","first-page":"1664","DOI":"10.1109\/TKDE.2009.205","volume":"22","author":"F Zhuang","year":"2010","unstructured":"F. Zhuang, P. Luo, H. Xiong, Y. Xiong, Q. He, Z. Shi, Cross-domain learning from multiple sources: A consensus regularization perspective. IEEE Trans. Knowl. Data Eng. 22(12), 1664\u20131678 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Recommender Systems Handbook"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-2197-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T07:54:29Z","timestamp":1736927669000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-1-0716-2197-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,2,24]]},"ISBN":["9781071621967","9781071621974"],"references-count":87,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_13","relation":{},"subject":[],"published":{"date-parts":[[2012,2,24]]},"assertion":[{"value":"24 February 2012","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}