Dissertation/Thesis Abstract

Enhancing Recommender Systems Using Social Indicators
by Gartrell, Charles M., Ph.D., University of Colorado at Boulder, 2014, 103; 3635830
Abstract (Summary)

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.

Indexing (document details)
Advisor: Han, Richard
Commitee: Black, John, Lv, Qin, Mishra, Shivakant, Paquet, Ulrich
School: University of Colorado at Boulder
Department: Computer Science
School Location: United States -- Colorado
Source: DAI-B 76/01(E), Dissertation Abstracts International
Subjects: Information science, Computer science
Keywords: Collaborative filtering, Group recommendation, Machine learning, Mobile computing, Recommender systems, Social networks
Publication Number: 3635830
ISBN: 9781321171556
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