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US08676736B2 Recommender systems and methods using modified alternating least squares algorithm 有权
推荐系统和使用修改的交替最小二乘法的方法

Recommender systems and methods using modified alternating least squares algorithm
Abstract:
A system and method of providing personalized item recommendations in a communication system comprising a server and a plurality of client devices. At the server, a plurality of user rating vectors are received from a plurality of client devices and aggregated into a rating matrix that is factorized into a user feature matrix and an item feature matrix, with the product of the user feature and item feature matrixes approximating the user rating matrix. The factorization comprises the steps of the ALS1 or the IALS1 algorithm including: initializing the user feature matrix and the item feature matrix with predefined initial values; alternately optimizing the user feature matrix and the item feature matrix until a termination condition is met. The item feature matrix is transmitted from the server to at least one client device, and a predictive rating vector is generated as the product of the associated user feature vector and the item feature matrix. At least one item is selected for recommendation to a user from the items associated with the predictive rating vector.
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