The problem faced by users in the fashion industry is the lack of a recommendation system that caters to their individual preferences and practical considerations. This lack of personalization leads to dissatisfaction with the outfits recommended by traditional recommendation systems, which often prioritize aesthetics over practicality. Users want a system that not only provides visually appealing outfits but also takes into account factors such as their physical attributes, style preferences, and cultural influences.
To address the above problem, Kaleidoscope Closet has developed a unique approach to personalized fashion guidance.
- The system leverages a specially curated dataset that captures the essence of stylish outfits.
- This dataset goes beyond simply listing items; it incorporates real-world examples by referencing outfits available from popular brands.
- This bridges the gap between theoretical recommendations and practical application, allowing users to see how suggested styles translate into tangible outfit options. The dataset includes images of the outfits, providing valuable visual context and inspiration.
- By incorporating price points as well, Kaleidoscope Closet ensures its recommendations are not just aesthetically pleasing but also financially accessible.
The working principle of Kaleidoscope Closet is as follows:
- The system collects user data, including physical attributes, style preferences, and cultural influences.
- The system then analyzes the user data and compares it with the dataset to identify the most suitable outfits.
- Next, the system generates personalized recommendations based on the identified outfits.
- The system presents the recommendations to the user, along with images and price points of the outfits.
- To ensure the accuracy and effectiveness of the recommendations, the system utilizes a combination of software tools and techniques which includes HTML, CSS, and Javascript for the front end, Excel to store the data, Flask for connection, and Python Programming Language for analysis and recommendation.
