TY - GEN
T1 - Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility
AU - Liao, Shuiying
AU - Ding, Yujuan
AU - Mok, P. Y.
N1 - Funding Information:
The work described in this paper is supported in part by a grant from the Research Grants Council of the Hong Kong SAR, China (Grant Number 152112/19E) and by the Innovation and Technology Commission of Hong Kong under grant ITP/028/21TP.
Publisher Copyright:
© 2023 ACM.
PY - 2023/6/12
Y1 - 2023/6/12
N2 - Fashion recommendation considers both product similarity and compatibility, and has drawn increasing research interest. It is a challenging task because it often needs to use information from different sources, such as visual content or textual descriptions for the prediction of user preferences. In terms of complementary recommendation, existing approaches were dedicated to modeling either product compatibility or users' personalization in a direct and decoupled manner, yet overlooked additional relations hidden within historical user-product interactions. In this paper, we propose a Normalized indirect Personal Compatibility modeling scheme based on Bayesian Personalized Ranking (NiPC-BPR) for mix-and-match clothing recommendations. We exploit direct and indirect personalization and compatibility relations from the user and product interactions, and effectively integrate various multi-modal data. Extensive experimental results on two benchmark datasets show that our method outperforms other methods by large margins.
AB - Fashion recommendation considers both product similarity and compatibility, and has drawn increasing research interest. It is a challenging task because it often needs to use information from different sources, such as visual content or textual descriptions for the prediction of user preferences. In terms of complementary recommendation, existing approaches were dedicated to modeling either product compatibility or users' personalization in a direct and decoupled manner, yet overlooked additional relations hidden within historical user-product interactions. In this paper, we propose a Normalized indirect Personal Compatibility modeling scheme based on Bayesian Personalized Ranking (NiPC-BPR) for mix-and-match clothing recommendations. We exploit direct and indirect personalization and compatibility relations from the user and product interactions, and effectively integrate various multi-modal data. Extensive experimental results on two benchmark datasets show that our method outperforms other methods by large margins.
KW - Compatibility
KW - Complementary Recommendation
KW - Fashion Recommendation
KW - Multi-modal.
KW - Personalization
UR - http://www.scopus.com/inward/record.url?scp=85163665252&partnerID=8YFLogxK
U2 - 10.1145/3591106.3592224
DO - 10.1145/3591106.3592224
M3 - Conference article published in proceeding or book
AN - SCOPUS:85163665252
T3 - ICMR 2023 - Proceedings of the 2023 ACM International Conference on Multimedia Retrieval
SP - 560
EP - 564
BT - ICMR 2023 - Proceedings of the 2023 ACM International Conference on Multimedia Retrieval
PB - Association for Computing Machinery, Inc
T2 - 2023 ACM International Conference on Multimedia Retrieval, ICMR 2023
Y2 - 12 June 2023 through 15 June 2023
ER -