A Review of Modern Fashion Recommender Systems

Author:

Deldjoo Yashar1ORCID,Nazary Fatemeh1ORCID,Ramisa Arnau2ORCID,McAuley Julian3ORCID,Pellegrini Giovanni1ORCID,Bellogin Alejandro4ORCID,Noia Tommaso Di1ORCID

Affiliation:

1. Polytechnic University of Bari, Italy

2. Amazon, USA

3. UC San Diego, USA

4. Autonomous University of Madrid, Spain

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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1. GNNctd: A graph neural network based on complicated temporal dependencies modeling for fashion trend prediction;Knowledge-Based Systems;2024-10

2. Multimodal Recommender Systems: A Survey;ACM Computing Surveys;2024-09-10

3. Text semantic matching algorithm based on the introduction of external knowledge under contrastive learning;International Journal of Machine Learning and Cybernetics;2024-07-24

4. Fuzzy Norm-Explicit Product Quantization for Recommender Systems;IEEE Transactions on Fuzzy Systems;2024-05

5. Revolutionizing Fashion Recommendations: A Deep Dive into Deep Learning-based Recommender Systems;Proceedings of the 7th International Conference on Networking, Intelligent Systems and Security;2024-04-18

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