Design of an interactive fashion recommendation platform with intelligent systems

Abstract

With the increase in customer expectations in online fashion sales, greater integration of fashion recommender systems (RSs) allows more personalization. Design decisions rely on personal taste, as well as many other external influences, such as trends and social media, making it challenging to adapt intelligent systems for the fashion industry. Different methods for recommending personalized fashion items have been proposed, however, the literature still lacks an approach for recommending expert-suggested and personalized items. In this research, an interactive web-based platform is developed to support personalized fashion styling, focusing on users with diverse body shapes. To merge the user’s taste and the expert’s suggestion, the proposed methodology in this research combines genetic algorithms and machine learning techniques allowing the system to access expert knowledge (including external influences) and incremental learning capability, by adapting to the user preferences that unfold during interaction with the system.