Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2019
Committee Chair or Co-Chairs
Jeff Knisley
Committee Members
Robert Gardner, Michele Joyner
Abstract
We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first deriving a network model from a recommender's similarity matrix then applying spectral learning techniques to produce a new network model. The response learned by the new version of the recommender can be meta information. This leads to a system capable of incorporating meta data into recommendations.
Document Type
Thesis - unrestricted
Recommended Citation
Williams, Alyssa, "Hybrid Recommender Systems via Spectral Learning and a Random Forest" (2019). Electronic Theses and Dissertations. Paper 3666. https://dc.etsu.edu/etd/3666
Copyright
Copyright by the authors.
Included in
Databases and Information Systems Commons, Other Mathematics Commons, Theory and Algorithms Commons