TY - GEN
T1 - Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation
AU - Liu, Jian
AU - Zhao, Pengpeng
AU - Zhuang, Fuzhen
AU - Liu, Yanchi
AU - Sheng, Victor S.
AU - Xu, Jiajie
AU - Zhou, Xiaofang
AU - Xiong, Hui
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
KW - Cross-domain Recommendation;Knowledge Transfer;Aesthetic Feature
UR - http://www.scopus.com/inward/record.url?scp=85086586261&partnerID=8YFLogxK
U2 - 10.1145/3366423.3380036
DO - 10.1145/3366423.3380036
M3 - Conference contribution
AN - SCOPUS:85086586261
T3 - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
SP - 2768
EP - 2774
BT - The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery, Inc
Y2 - 20 April 2020 through 24 April 2020
ER -