Modeling Field-Level Factor Interactions for Fashion Recommendation

Yujuan Ding, P. Y. Mok, Xun Yang, Yanghong Zhou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due to the sparsity of the interaction data and the diversity of user preference in fashion. To tackle the challenge, this paper investigates multiple factor fields in fashion domain, such as colour, style, brand, and tries to specify the implicit user-item interaction into field level. Specifically, an attentional factor field interaction graph (AFFIG) approach is proposed which models both the user-factor interactions and cross-field factors interactions for predicting the recommendation probability at specific field. In addition, an attention mechanism is equipped to aggregate the cross-field factor interactions for each field. Extensive experiments have been conducted on three E-Commerce fashion datasets and the results demonstrate the effectiveness of the proposed method for fashion recommendation. The influence of various factor fields on recommendation in fashion domain is also discussed through experiments.

Original languageEnglish
Title of host publicationICME 2022 - IEEE International Conference on Multimedia and Expo 2022, Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781665485630
ISBN (Print)9781665485647
Publication statusPublished - 26 Aug 2022
Event2022 IEEE International Conference on Multimedia and Expo, ICME 2022 - Taipei, Taiwan
Duration: 18 Jul 202222 Jul 2022

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X


Conference2022 IEEE International Conference on Multimedia and Expo, ICME 2022


  • Attribute Incorporation
  • Factor Interaction Modeling
  • Personalized Fashion Recommendation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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