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.
