Patent Document ID: 20120030159
Application ID: 13194318
Patent Flag: 0

Claim One:
1. A method of providing personalized item recommendations in a communication system comprising a server and a plurality of client devices, the method comprising at the server, receiving a plurality of user rating vectors r u from a plurality of said client devices, at the server, aggregating the user rating vectors r u into a rating matrix R, at the server, factorizing said rating matrix R into a user feature matrix P and an item feature matrix Q, wherein the product {circumflex over (R)} of said user feature matrix P and said item feature matrix Q approximates the user rating matrix R, said factorization comprising the steps of: initializing said user feature matrix P and said item feature matrix Q with predefined initial values, alternately optimizing said user feature matrix P and said item feature matrix Q until a termination condition is met, wherein (i) in a P-step, a cost function F ( p u ) = λ p u T p u + ∑ i = 1 M  c ui ( p u T q i - r ui ) 2 is minimized for each user feature vector p u of said user feature matrix P with a fixed item feature matrix Q, and (ii) in a Q-step, a cost function F ( q i ) = λ q i T q i + ∑ i = 1 N  c ui ( p u T q i - r ui ) 2 is minimized for each item feature vector q i of said item feature matrix Q with a fixed user feature matrix P, wherein λ is a preset regularization factor, c ui is a confidence value with respect to user u and item i, and r ui is the rating value for an item i evaluated by a user u, wherein in each of the P-steps, each scalar element of p u is computed from all other scalar elements of p u and the fixed matrix Q, and similarly, in each of the Q-steps, each scalar element of q i , is computed from all other scalar elements of q i and the fixed matrix P, and, transmitting, from the server, said item feature matrix Q to at least one client device, generating a predictive rating vector {circumflex over (r)} u as the product of the associated user feature vector p u and the item feature matrix Q, and by using a predefined selection scheme, selecting at least one item for recommendation to user from the items associated with the predictive rating vector {circumflex over (r)} u .