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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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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.