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Abstract

A method and apparatus are disclosed for recommending items of interest by fusing a plurality of recommendation scores from individual recommendation tools using one or more Radial Basis Function neural networks. The Radial Basis Function neural networks include N inputs and at least one output, interconnected by a plurality of hidden units in a hidden layer. A unique neural network can be used for each user, or a neural network can be shared by a plurality of users, such as a set of users having similar characteristics. A neural network training process initially trains each Radial Basis Function neural network using data from a training data set. A neural network cross-validation process selects the Radial Basis Function neural network that performs best on the cross-validation data set. A neural network program recommendation process uses the selected neural network(s) to recommend items of interest to a user.

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First Claim