Patents by Inventor András Németh Bottyán

András Németh Bottyán has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Recommender systems and methods using modified alternating least squares algorithm
    Patent number: 8676736
    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.
    Type: Grant
    Filed: July 29, 2011
    Date of Patent: March 18, 2014
    Assignee: Gravity Research and Development Kft.
    Inventors: István Pilászy, Domonkos Tikk, Gábor Takács, András Németh Bottyán, Dávid Zibriczky
  • Recommender Systems and Methods
    Publication number: 20120030159
    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.
    Type: Application
    Filed: July 29, 2011
    Publication date: February 2, 2012
    Applicant: Gravity Research & Development Kft.
    Inventors: István Pilászy, Domonkos Tikk, Gábor Takács, András Németh Bottyán, Dávid Zibriczky