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Pre-Defense
9/9/2013 04:00 pm
CoRE B (Room 305)

UNDERSTANDING PREFERENCES AND SIMILARITIES FROM USER-AUTHORED TEXT: APPLICATIONS TO SEARCH AND RECOMMENDATIONS

Gayatree Ganu, Rutgers

Defense Committee: Amélie Marian (Chair), Alex Borgida, Tina Eliassi-Rad, Daniel Tunkelang (Head of Query Understanding, LinkedIn)

Abstract

Users rely increasingly on online reviews, forums and blogs to exchange information, practical tips, and stories. However, such user-authored content is in a free text format, usually with very scant structured metadata information, and is therefore difficult for computers to understand, analyze, and aggregate. Users often face the daunting task of accessing and reading a large quantity of text to discover potentially useful information. This work addresses the need to automatically leverage useful information from a large quantity of user-authored text to improve search and to provide personalized recommendations.

In my research, I first identify topical and sentiment information from free-form text, and use these to make accurate text-based recommendations and to make fine-grained predictions of user sentiments towards product features. Secondly, I learn similarities between online users via multiple indicators like similar information needs, user profiles, or topics of interest to predict future social interactions and to improve ranking in keyword search. I then propose an efficient search over user authored content at multiple levels of granularity to enable finding the precise information without having to read a large quantity of text.