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A Review of Modern Fashion Recommender Systems

ACM Computing Surveys, ISSN: 1557-7341, Vol: 56, Issue: 4, Page: 1-37
2024
  • 24
    Citations
  • 0
    Usage
  • 142
    Captures
  • 1
    Mentions
  • 14
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    24
    • Citation Indexes
      24
  • Captures
    142
  • Mentions
    1
    • News Mentions
      1
      • News
        1
  • Social Media
    14
    • Shares, Likes & Comments
      14
      • Facebook
        14

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Data on Computing Reported by Researchers at Polytechnic University of Bari (A Review of Modern Fashion Recommender Systems)

2024 MAR 29 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- Investigators publish new report on Computers - Computing. According

Article Description

The textile and apparel industries have grown tremendously over the past few years. Customers no longer have to visit many stores, stand in long queues, or try on garments in dressing rooms, as millions of products are now available in online catalogs. However, given the plethora of options available, an effective recommendation system is necessary to properly sort, order, and communicate relevant product material or information to users. Effective fashion recommender systems (RSs) can have a noticeable impact on billions of customers' shopping experiences and increase sales and revenues on the provider side.The goal of this survey is to provide a review of RSs that operate in the specific vertical domain of garment and fashion products. We have identified the most pressing challenges in fashion RS research and created a taxonomy that categorizes the literature according to the objective they are trying to accomplish (e.g., item or outfit recommendation, size recommendation, and explainability, among others) and type of side information (users, items, context). We have also identified the most important evaluation goals and perspectives (outfit generation, outfit recommendation, pairing recommendation, and fill-in-the-blank outfit compatibility prediction) and the most commonly used datasets and evaluation metrics.

Bibliographic Details

Yashar Deldjoo; Fatemeh Nazary; Giovanni Pellegrini; Tommaso Di Noia; Arnau Ramisa; Julian McAuley; Alejandro Bellogin

Association for Computing Machinery (ACM)

Mathematics; Computer Science

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