Learning to Rank through User Interest Mining: Towards Search Personalization

In online shopping, search results often have inherent ambiguity. Two customers using the same term as search query might have completely different expectations of the displayed results. For example, when the users type in the query “headphone”, some of them might expect over-ear headphone with passive noise isolation, while others might expect in-ear headphone with better portability. This project aims to extract users’ interests or preferences and understand what they want. After discovering users’ interests, it is possible to provide personalized search results for everyone by using machine learning techniques.
The company will be able to attract and keep more customers by providing innovative and personalized online shopping experience.

Faculty Supervisor:

Roger Grosse


Zhou Fang


Loblaw Company Limited


Computer science


Service industry


University of Toronto



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