Title page for ETD etd-0501102-130405

Document Type	thesis
Author Name	Kogel, Wendy E.
URN	etd-0501102-130405
Title	Faster Training of Neural Networks for Recommender Systems
Degree	MS
Department	Computer Science
Advisors	Professor Carolina Ruiz, Advisor Professor Sergio A. Alvarez, Advisor Professor Lee A. Becker, Reader Professor Micha Hofri, Department Head
Keywords	mixture of experts recommender systems neural networks
Date of Presentation/Defense	2002-04-18
Availability	unrestricted
Abstract In this project we investigate the use of artificial neural networks(ANNs) as the core prediction function of a recommender system. In the past, research concerned with recommender systems that use ANNs have mainly concentrated on using collaborative-based information. We look at the effects of adding content-based information and how altering the topology of the network itself affects the accuracy of the recommendations generated. In particular, we investigate a mixture of experts topology. We create two expert clusters in the hidden layer of the ANN, one for content-based data and another for collaborative-based data. This greatly reduces the number of connections between the input and hidden layers. Our experimental evaluation shows that this new architecture produces the same accuracy of recommendation as the fully connected configuration with a large decrease in the amount of time it takes to train the network. This decrease in time is a great advantage because of the need for recommender systems to provide real time results to the user.
Files	wkogel.pdf

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