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
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.
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