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1
Introduction
2
Major research topics
3
Fashion recommendation
4
Magicclose
5
First criterion
6
Second criterion
7
Database
8
Criteria
9
Data
10
Results
11
Visualization
12
Pairing
13
Demo
14
Comments
15
Recommendations
16
Photoresist
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Beauty Expo
18
Factors
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Data Collection
20
Beauty Edge Views
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Beauty Attributes
22
Fully Connected Block
23
Tree Structure
24
Sugar Crop
25
Energy Function
26
Comparison
27
Future Work
Description:
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.

Towards Intelligent Fashion Recommendation

University of Central Florida
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