research-article

Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation

Authors:

Victor S. Sheng,

Hui XiongAuthors Info & Claims

WWW '20: Proceedings of The Web Conference 2020

Pages 2768 - 2774

https://doi.org/10.1145/3366423.3380036

Published: 20 April 2020 Publication History

Abstract

Visual aesthetics of products plays an important role in the decision process when purchasing appearance-first products, e.g., clothes. Indeed, user’s aesthetic preference, which serves as a personality trait and a basic requirement, is domain independent and could be used as a bridge between domains for knowledge transfer. However, existing work has rarely considered the aesthetic information in product images for cross-domain recommendation. To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains. Specifically, we first leverage an aesthetic network to extract aesthetic features. Then, we integrate these features into a cross-domain network to transfer users’ domain independent aesthetic preferences. Moreover, network cross-connections are introduced to enable dual knowledge transfer across domains. Finally, the experimental results on real-world datasets show that our proposed model ACDN outperforms benchmark methods in terms of recommendation accuracy.

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Cited By

Zhu FYang XLi LZhou JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)An Active Masked Attention Framework for Many-to-Many Cross-Domain RecommendationsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681435(9680-9689)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681435
Yang WJian YWang YLu SShen LWang BTang HZhang LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645357(3195-3206)Online publication date: 13-May-2024
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Liu JSun LNie WSu YZhang YLiu A(2024)Inter- and Intra-Domain Potential User Preferences for Cross-Domain RecommendationIEEE Transactions on Multimedia10.1109/TMM.2024.337457726(8014-8025)Online publication date: 2024
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Index Terms

Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '20: Proceedings of The Web Conference 2020

April 2020

3143 pages

ISBN:9781450370233

DOI:10.1145/3366423

Editors:
Yennun Huang
Acadmica sinica, Taiwan
,
Irwin King
The Chinese University of Hong Kong, Hong Kong
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 20 April 2020

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Cross-domain Recommendation;Knowledge Transfer;Aesthetic Feature

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WWW '20

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SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Zhu FYang XLi LZhou JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)An Active Masked Attention Framework for Many-to-Many Cross-Domain RecommendationsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681435(9680-9689)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681435
Yang WJian YWang YLu SShen LWang BTang HZhang LChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive LearningProceedings of the ACM Web Conference 202410.1145/3589334.3645357(3195-3206)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645357
Liu JSun LNie WSu YZhang YLiu A(2024)Inter- and Intra-Domain Potential User Preferences for Cross-Domain RecommendationIEEE Transactions on Multimedia10.1109/TMM.2024.337457726(8014-8025)Online publication date: 2024
https://doi.org/10.1109/TMM.2024.3374577
Liu HHe JXu ZZhang YZhao Z(2024)Efficient and adaptive secure cross-domain recommendationsExpert Systems with Applications10.1016/j.eswa.2024.125154(125154)Online publication date: Aug-2024
https://doi.org/10.1016/j.eswa.2024.125154
Guo WZhuang FZhang XTong YDong J(2024)A comprehensive survey of federated transfer learning: challenges, methods and applicationsFrontiers of Computer Science10.1007/s11704-024-40065-x18:6Online publication date: 23-Jul-2024
https://doi.org/10.1007/s11704-024-40065-x
Zang TZhu YZhang RZhu JTang F(2024)Explicitly modeling relationships between domain-specific and domain-invariant interests for cross-domain recommendationWorld Wide Web10.1007/s11280-024-01305-z27:6Online publication date: 28-Oct-2024
https://doi.org/10.1007/s11280-024-01305-z
Zhao PJin YRen XLi Y(2024)A personalized cross-domain recommendation with federated meta learningMultimedia Tools and Applications10.1007/s11042-024-18495-383:28(71435-71450)Online publication date: 8-Feb-2024
https://doi.org/10.1007/s11042-024-18495-3
Zhang FZhou YSun PXu YHan WHuang HChen J(2024)CRAS: cross-domain recommendation via aspect-level sentiment extractionKnowledge and Information Systems10.1007/s10115-024-02130-666:9(5459-5477)Online publication date: 1-Sep-2024
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Wang JYuan FCheng MJose JYu CKong BWang ZHu BLi Z(2024)TransRec: Learning Transferable Recommendation from Mixture-of-Modality FeedbackWeb and Big Data10.1007/978-981-97-7235-3_13(193-208)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_13
Chen GZhang XSu YLai YXiang JZhang JZheng YWilliams BChen YNeville J(2023)Win-winProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25531(4149-4156)Online publication date: 7-Feb-2023
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