Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150977
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dc.contributor.authorZhang, Yinanen_US
dc.contributor.authorLiu, Yongen_US
dc.contributor.authorHan, Pengen_US
dc.contributor.authorMiao, Chunyanen_US
dc.contributor.authorCui, Lizhenen_US
dc.contributor.authorLi, Baolien_US
dc.contributor.authorTang, Haihongen_US
dc.date.accessioned2021-06-09T03:56:19Z-
dc.date.available2021-06-09T03:56:19Z-
dc.date.issued2020-
dc.identifier.citationZhang, Y., Liu, Y., Han, P., Miao, C., Cui, L., Li, B. & Tang, H. (2020). Learning personalized itemset mapping for cross-domain recommendation. 2020 International Joint Conference on Artificial Intelligence (IJCAI’20), 2561-2567. https://dx.doi.org/10.24963/ijcai.2020/355en_US
dc.identifier.isbn978-0-9992411-6-5-
dc.identifier.urihttps://hdl.handle.net/10356/150977-
dc.description.abstractCross-domain recommendation methods usually transfer knowledge across different domains implicitly, by sharing model parameters or learning parameter mappings in the latent space. Differing from previous studies, this paper focuses on learning explicit mapping between a user's behaviors (i.e. interaction itemsets) in different domains during the same temporal period. In this paper, we propose a novel deep cross-domain recommendation model, called Cycle Generation Networks (CGN). Specifically, CGN employs two generators to construct the dual-direction personalized itemset mapping between a user's behaviors in two different domains over time. The generators are learned by optimizing the distance between the generated itemset and the real interacted itemset, as well as the cycle-consistent loss defined based on the dual-direction generation procedure. We have performed extensive experiments on real datasets to demonstrate the effectiveness of the proposed model, comparing with existing single-domain and cross-domain recommendation methods.en_US
dc.description.sponsorshipAI Singaporeen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.rights© 2020 Twenty-Fifth International Joint Conference on Artificial Intelligence Organization (IJCAI-16 Organization)]. All rights reserved. This paper was published in 2020 International Joint Conference on Artificial Intelligence (IJCAI’20) and is made available with permission of Twenty-Fifth International Joint Conference on Artificial Intelligence Organization (IJCAI-16 Organization).en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleLearning personalized itemset mapping for cross-domain recommendationen_US
dc.typeConference Paperen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.contributor.conference2020 International Joint Conference on Artificial Intelligence (IJCAI’20)en_US
dc.contributor.researchJoint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY)en_US
dc.identifier.doi10.24963/ijcai.2020/355-
dc.description.versionPublished versionen_US
dc.identifier.spage2561en_US
dc.identifier.epage2567en_US
dc.subject.keywordsMachine Learningen_US
dc.subject.keywordsRecommender Systemsen_US
dc.description.acknowledgementThis research is supported, in part, by the National Research Foundation, Prime Minister’s Office, Singapore under its AI Singapore Programme (AISG Award No: AISG-GC-2019- 003) and under its NRF Investigatorship Programme (NRFI Award No. NRF-NRFI05-2019-0002). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of National Research Foundation, Singapore. This research is also supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), Nanyang Technological University, Singapore.en_US
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