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Tied Boltzmann Machines for Cold Start Recommendations

Asela Gunawardana and Christopher Meek

Abstract

We describe a novel statistical model, the tied Boltzmann machine, for combining collaborative and content information for recommendations. In our model, pairwise interactions between items are captured through a Boltzmann machine, whose parameters are constrained according to the content associated with the items. This allows the model to use content information to recommend items that are not seen during training. We describe a tractable algorithm for training the model, and give experimental results evaluating the model in two cold start recommendation tasks on the MovieLens data set.

Details

Publication typeInproceedings
Published inACM International Conference on Recommender Systems
URLhttp://www.acm.org/
PublisherAssociation for Computing Machinery, Inc.
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