Machine Learning based System Suggested Critiquing for in-car Conversational Recommendation

Car Infotainment and Navigation systems to date have only basic interactivity and functionality, and there is nothing in the car today that will keep drivers aware of their surroundings outside the car when the car is in a fully autonomous driving mode. Therefore, we want to contribute in the development of a new in-car infotainment system that is both interactive, personal, contextual (aware of the car’s location and surrounding services). More specifically, we aim to extend iNAGO’s existing conversational recommendation engine to the one that better understands user intent and proactively suggest the correct choices that navigate her to the point of her interest in an effective and efficient manner.

Faculty Supervisor:

Scott Sanner

Student:

Arpit Rana

Partner:

iNAGO

Discipline:

Engineering - mechanical

Sector:

Information and cultural industries

University:

University of Toronto