Towards Intelligent Fashion Recommendation

Explore the world of intelligent fashion recommendation in this guest presentation by Dr. Shuicheng Yan from the University of Central Florida. Delve into major research topics, including fashion recommendation systems, database criteria, and visualization techniques. Learn about innovative approaches such as the Magic Close algorithm, beauty attributes analysis, and fully connected block structures. Discover how data collection, energy functions, and tree structures contribute to more accurate and personalized fashion recommendations. Gain insights into the future of AI-driven fashion advice and its potential impact on the industry. Examine real-world applications through demos and case studies, including the Beauty Expo concept. Understand the factors influencing fashion recommendations and how they can be optimized for better user experiences.

Introduction
Major research topics
Fashion recommendation
Magicclose
First criterion
Second criterion
Database
Criteria
Data
Results
Visualization
Pairing
Demo
Comments
Recommendations
Photoresist
Beauty Expo
Factors
Data Collection
Beauty Edge Views
Beauty Attributes
Fully Connected Block
Tree Structure
Sugar Crop
Energy Function
Comparison
Future Work