Show me the best outfit for a certain scene: a scene-aware fashion recommender system

Tangwei Ye, Liang Hu*, Qi Zhang, Zhong Yuan Lai, Usman Naseem, Dora D. Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)

Abstract

Fashion recommendation (FR) has received increasing attention in the research of new types of recommender systems. Existing fashion recommender systems (FRSs) typically focus on clothing item suggestions for users in three scenarios: 1) how to best recommend fashion items preferred by users; 2) how to best compose a complete outfit, and 3) how to best complete a clothing ensemble. However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. More specifically, our SAFRS addresses this problem by encoding scene and outfit information in separation attention encoders and then fusing the resulting feature embeddings via a novel scene-aware compatibility score function. Extensive qualitative and quantitative experiments are conducted to show that our SAFRS model outperforms all baselines for every evaluated metric.

Original languageEnglish
Title of host publicationThe ACM Web Conference 2023
Subtitle of host publicationproceedings of the World Wide Web Conference WWW 2023
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1172-1180
Number of pages9
ISBN (Electronic)9781450394161
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23

Keywords

  • fashion recommender system
  • scene-aware fashion recommender system
  • visual transformer

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