Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility

Shuiying Liao, Yujuan Ding, P. Y. Mok

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

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICMR 2023 - Proceedings of the 2023 ACM International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages560-564
Number of pages5
ISBN (Electronic)9798400701788
DOIs
Publication statusPublished - 12 Jun 2023
Event2023 ACM International Conference on Multimedia Retrieval, ICMR 2023 - Thessaloniki, Greece
Duration: 12 Jun 202315 Jun 2023

Publication series

NameICMR 2023 - Proceedings of the 2023 ACM International Conference on Multimedia Retrieval

Conference

Conference2023 ACM International Conference on Multimedia Retrieval, ICMR 2023
Country/TerritoryGreece
CityThessaloniki
Period12/06/2315/06/23

Keywords

  • Compatibility
  • Complementary Recommendation
  • Fashion Recommendation
  • Multi-modal.
  • Personalization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software
  • Computer Graphics and Computer-Aided Design

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