Abstract: A method of predicting user-item ratings includes providing a first matrix of hidden variables associated with individual items, a second matrix of hidden variables associated with individual users, a third matrix of predicted user-item ratings derived from an inner product of vectors in the first and second matrices, and a fourth matrix of actual user-item ratings. The first and second matrices are alternately fixed and solved with a weighted-? regularization of at least one of the first and second matrices by minimizing a sum of squared errors between actual user-item ratings in the fourth matrix and corresponding predicted user-item ratings in the third matrix repeatedly until a stopping criterion is satisfied.

Abstract: A method of predicting user-item ratings includes providing a first matrix of hidden variables associated with individual items, a second matrix of hidden variables associated with individual users, a third matrix of predicted user-item ratings derived from an inner product of vectors in the first and second matrices, and a fourth matrix of actual user-item ratings. The first and second matrices are alternately fixed and solved with a weighted-? regularization of at least one of the first and second matrices by minimizing a sum of squared errors between actual user-item ratings in the fourth matrix and corresponding predicted user-item ratings in the third matrix repeatedly until a stopping criterion is satisfied.

Abstract: A method of finding genes associated with a disease, includes: finding all potential gene symbols; folding at least one alias into official gene symbols; and computing the relevance of each official symbol to the disease. The method may further include, eliminating non-gene symbols by use of contextual clues.