Learning personalized itemset mapping for cross-domain recommendation

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. i...

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Main Authors: Zhang, Yinan, Liu, Yong, Han, Peng, Miao, Chunyan, Cui, Lizhen, Li, Baoli, Tang, Haihong
Other Authors: School of Computer Science and Engineering
Format: Conference or Workshop Item
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/150977
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1509772021-06-09T03:56:19Z Learning personalized itemset mapping for cross-domain recommendation Zhang, Yinan Liu, Yong Han, Peng Miao, Chunyan Cui, Lizhen Li, Baoli Tang, Haihong School of Computer Science and Engineering 2020 International Joint Conference on Artificial Intelligence (IJCAI’20) Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Machine Learning Recommender Systems 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. AI Singapore National Research Foundation (NRF) Published version This 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. 2021-06-09T03:56:19Z 2021-06-09T03:56:19Z 2020 Conference Paper 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.2020/355 978-0-9992411-6-5 https://hdl.handle.net/10356/150977 10.24963/ijcai.2020/355 2561 2567 en © 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). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Machine Learning
Recommender Systems
spellingShingle Engineering::Computer science and engineering
Machine Learning
Recommender Systems
Zhang, Yinan
Liu, Yong
Han, Peng
Miao, Chunyan
Cui, Lizhen
Li, Baoli
Tang, Haihong
Learning personalized itemset mapping for cross-domain recommendation
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Zhang, Yinan
Liu, Yong
Han, Peng
Miao, Chunyan
Cui, Lizhen
Li, Baoli
Tang, Haihong
format Conference or Workshop Item
author Zhang, Yinan
Liu, Yong
Han, Peng
Miao, Chunyan
Cui, Lizhen
Li, Baoli
Tang, Haihong
author_sort Zhang, Yinan
title Learning personalized itemset mapping for cross-domain recommendation
title_short Learning personalized itemset mapping for cross-domain recommendation
title_full Learning personalized itemset mapping for cross-domain recommendation
title_fullStr Learning personalized itemset mapping for cross-domain recommendation
title_full_unstemmed Learning personalized itemset mapping for cross-domain recommendation
title_sort learning personalized itemset mapping for cross-domain recommendation
publishDate 2021
url https://hdl.handle.net/10356/150977
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