Enhancing Recommender Systems Using Social Indicators

Publication Year:
2014

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Repository URL:
https://scholar.colorado.edu/csci_gradetds/3
Author(s):
Gartrell, Charles Michael
Tags:
collaborative filtering; group recommendation; machine learning; mobile computing; recommender systems; social networks; Computer Sciences
thesis / dissertation description
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.