In recent years, Recommendation Systems have revolutionized how social media and ecommerce are used. Fashion Recommendation Systems have made it easier for customers to do shopping, by recommending items to them based on various factors, such as their previous orders, and their similarities to other users. To apply a Fashion Recommendation System, there are four main approaches: Content-based filtering, where the system recommends similar items to the user. Collaborative filtering, in which the system recommends items from similar users. Hybrid filtering, which merges the features of the previous techniques, and Hyper-personalized filtering, which uses the profiling of customers to draw certain assumptions about users. The problem this research addresses is the lack of involving the intent of users when designing and applying a fashion recommendation system, as well as the cold start problem. The Research Questions are: 1. How to develop and implement a Fashion Recommendation System as an artifact that provides recommendations to customers, 2. How to implement intent as context in such Recommendation Systems to provide improved recommendations to the fashion customers, 3. How the inclusion of intent as context in a Fashion Recommendation System impacts customer satisfaction. The Research Methodology used in this study is design science research, with various research strategies and data collection methods used throughout, such as crowdsourcing, document analysis, testing, qualitative questionnaires, and thematic analysis. The Results of the study indicate the involvement of the intent results in better recommendations, a smoother and more accurate shopping experience, and an overall higher customer satisfaction.