Distributed collaborative recommendation engine for Asset Store

In this project we attempt to research and develop from ground up a scalable distributed computing based recommendation engine using machine learning. A computer science student from the University of Toronto will work with Side Effects Software at their Toronto office to implement the research intensive recommendation engine algorithm and integrate it in the smart asset online store. We expect and hope that this will result in high quality recommendation, is scalable and has a strong foundation in statistical machine learning based algorithm approach.

Faculty Supervisor:

Dr. Eugene Fiume

Student:

Abbas Attarwala

Partner:

Side Effects Software

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Toronto

Program:

Accelerate

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