Abstract
The digitization of the fashion industry diversified consumer segments, and consumers now have broader choices with shorter production cycles; digital technology in the fashion industry is attracting the attention of consumers. Therefore, a system that efficiently supports the searching and recommendation of a product is becoming increasingly important. However, the text-based search method has limitations because of the nature of the fashion industry, in which design is a very important factor. Therefore, we developed an intelligent fashion technique based on deep learning for efficient fashion product searches and recommendations consisting of a Sketch-Product fashion retrieval model and vector-based user preference fashion recommendation model. It was found that the “Precision at 5” of the image-based similar product retrieval model was 0.774 and that of the sketch-based similar product retrieval model was 0.445. The vector-based preference fashion recommendation model also showed positive performance. This system is expected to enhance consumers’ satisfaction by supporting users in more effectively searching for fashion products or by recommending fashion products before they begin a search.
Highlights
The size of the domestic and overseas fashion market is steadily increasing
This study developed intelligent fashion techniques for Sketch-Product and personalized coordination. This system is composed of a Sketch-Product fashion retrieval model that can resolve the limitations of a picture image search, overcoming the restrictions of a traditional text-based search method, as well as a vector-based user preferred fashion recommendation model, applying an implicit user fashion profiling method that is different from the conventional profiling method to overcome the limitations of the preferred fashion profiling
This study developed intelligent fashion techniques for Sketch-Product and personalized coordination composed of a Sketch-Product fashion retrieval model that overcomes the limitations of a text-based search method, as well as a vector-based user preferred fashion recommendation model applying an implicit user fashion profiling method to overcome the limitations of the preferred fashion profiling to search for and recommend fashion products
Summary
The size of the domestic and overseas fashion market is steadily increasing. It is expected that the size of the domestic fashion market will grow by 8.8% in 2024 compared to its size in 2020, resulting in a market size of USD (United States dollars) $26.288 million [1,2]. Because of the nature of the fashion industry, which has a strong design factor, a text-based search method has limitations in providing satisfactory search results. Several systems such as “shopping how” and “smart lens” that search for products using fashion photographs were implemented, but they did not achieve satisfactory or meaningful performance. This study developed intelligent fashion techniques for Sketch-Product and personalized coordination This system is composed of a Sketch-Product fashion retrieval model that can resolve the limitations of a picture image search, overcoming the restrictions of a traditional text-based search method, as well as a vector-based user preferred fashion recommendation model, applying an implicit user fashion profiling method that is different from the conventional profiling method to overcome the limitations of the preferred fashion profiling.
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