A3-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction

Page view(s)
30
Checked on Apr 21, 2024
A3-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction
Title:
A3-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction
Journal Title:
IEEE Transactions on Multimedia
Publication Date:
16 February 2021
Citation:
Zhan, H., Lin, J., Ak, K. E., Shi, B., Duan, L.-Y., & Kot, A. C. (2021). A3-FKG: Attentive Attribute-Aware Fashion Knowledge Graph for Outfit Preference Prediction. IEEE Transactions on Multimedia, 1–1. doi:10.1109/tmm.2021.3059514
Abstract:
With the booming development of the online fashion industry, effective personalized recommender systems have become indispensable for the convenience they brought to the customers and the profits to the e-commercial platforms. Estimating the user’s preference towards the outfit is at the core of a personalized recommendation system. Existing works on fashion recommendation are largely centering on modelling the clothing compatibility without considering the user factor or characterizing the user’s preference over the single item. However, how to effectively model the outfits with either few or even none interactions, is yet under-explored. In this paper, we address the task of personalized outfit preference prediction via a novel Attentive Attribute-Aware Fashion Knowledge Graph (A3-FKG), which is incorporated to build the association between different outfits with both outfit- and item- level attributes. Additionally, a two-level attention mechanism is developed to capture the user’s preference: 1) User-specific relation-aware attention layer, which captures the user’s fine-grained preferences with different focus on relations for learning outfit representation; 2) Target-aware attention layer, which characterizes the user’s latent diverse interests from his/her behavior sequences for learning user representation. Extensive experiments conducted on a large-scale fashion outfit dataset demonstrate significant improvements over other methods, which verify the excellence of our proposed framework.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - AME Programmatic Funds
Grant Reference no. : A1892b0026
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1520-9210
1941-0077
Files uploaded:

File Size Format Action
a3kg-finalversion.pdf 1.93 MB PDF Open