Patent ID: 6687696
Filing Date: 2004-02-03
Classification: G06F,Y10S

Abstract:
A method in a computer system for training a latent class model comprising the steps:receiving data in the form of a list of tupels of entities; receiving a list of parameters, including a number of dimensions to be used in the model training, a predetermined termination condition, and a predetermined fraction of hold out data; splitting the dataset into training data and hold out data according to the predetermined fraction of hold out data; applying Tempered Expectation Maximization to the data to train a plurality of latent class models according to the following steps: computing tempered posterior probabilities for each tupel and each possible state of a corresponding latent class variable; using these posterior probabilities, updating class conditional probabilities for items, descriptors and attributes, and users; iterating the steps of computing tempered posterior probabilities and updating class conditional probabilities until the predictive performance on the hold-out data degrades; and adjusting the temperature parameter and continuing at the step of computing tempered posterior probabilities until the predetermined termination condition is met; and combining the trained models of different dimensionality into a single model by linearly combining their estimated probabilities.