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AI-driven detection of anomalous supplier profiles at web scale
Information gathered from internet sources has high variance and different types of noise. This causes out-of-distribution problems with downstream ML modules such as category classification and keyword extraction. The extremely large size of internet-scale datasets requires a solution that is efficient and scalable. The objective of this project is to develop a start-of-the-art anomaly detection system. The system must have low false positive rate,
Faculty Supervisor:
Mark Chignell
Student:
Partner:
Tealbook
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
University of Toronto
Program:
Accelerate
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