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. This recommendation engine when developed will be first of its kind for 3D smart digital assets. 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:

Eugene Fiume

Student:

Partner:

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate