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https://hdl.handle.net/10356/150977
Title: | Learning personalized itemset mapping for cross-domain recommendation | Authors: | Zhang, Yinan Liu, Yong Han, Peng Miao, Chunyan Cui, Lizhen Li, Baoli Tang, Haihong |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2020 | Source: | Zhang, 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.2020/355 | Conference: | 2020 International Joint Conference on Artificial Intelligence (IJCAI’20) | Abstract: | Cross-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. | URI: | https://hdl.handle.net/10356/150977 | ISBN: | 978-0-9992411-6-5 | DOI: | 10.24963/ijcai.2020/355 | Schools: | School of Computer Science and Engineering | Research Centres: | Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) | 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). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Conference Papers |
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