Date of Award

Summer 7-18-2014

Document Type

Thesis

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Richard Han

Second Advisor

Shivakant Mishra

Third Advisor

Qin Lv

Abstract

Recommender systems are increasingly driving user experiences on the Internet. In recent years, online social networks have quickly become the fastest growing part of the Web. The rapid growth in social networks presents a substantial opportunity for recommender systems to leverage social data to improve recommendation quality, both for recommendations intended for individuals and for groups of users who consume content together. This thesis shows that incorporating social indicators improves the predictive performance of group-based and individual-based recommender systems. We analyze the impact of social indicators through small-scale and large-scale studies, implement and evaluate new recommendation models that incorporate our insights, and demonstrate the feasibility of using these social indicators and other contextual data in a deployed mobile application that provides restaurant recommendations to small groups of users.

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