Patents by Inventor Jaya B. Kawale

Jaya B. Kawale 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).

  • Patent number: 10332015
    Abstract: Particle Thompson Sampling for online matrix factorization recommendation is described. In one or more implementations, a recommendation system provides a recommendation of an item to a user using Thompson Sampling. The recommendation system then receives a rating of the item from the user. Unlike conventional solutions which only update the user latent features, the recommendation system updates both user latent features and item latent features in a matrix factorization model based on the rating of the item. The updating is performed in real time which enables the recommendation system to quickly adapt to the user ratings to provide new recommendations. In one or more implementations, to update the user latent features and the item latent features in the matrix factorization model, the recommendation system utilizes a Rao-Blackwellized particle filter for online matrix factorization.
    Type: Grant
    Filed: October 16, 2015
    Date of Patent: June 25, 2019
    Assignee: Adobe Inc.
    Inventors: Jaya B. Kawale, Branislav Kveton, Hung H. Bui
  • Publication number: 20170140417
    Abstract: Campaign effectiveness determination techniques and systems are described that are usable to determine campaign effectiveness with improved accuracy and computing performance by reduction of confounding bias through dimension reduction. In one example, campaign data that pertains to first and second campaign groups is characterized using a plurality of features that describe subjects included in the first and second campaign groups. The characterized campaign data is projected, automatically and without user intervention, for the first and second campaign groups into a reduced dimension space, e.g., using linear or non-linear techniques. Subjects in the first and second campaign groups are associated, one to another using the projected campaign data, such that a number of subjects in the first campaign group is matched against a number of subjects in the second campaign group.
    Type: Application
    Filed: November 18, 2015
    Publication date: May 18, 2017
    Inventors: Sheng Li, Nikolaos Vlassis, Jaya B. Kawale
  • Publication number: 20170109642
    Abstract: Particle Thompson Sampling for online matrix factorization recommendation is described. In one or more implementations, a recommendation system provides a recommendation of an item to a user using Thompson Sampling. The recommendation system then receives a rating of the item from the user. Unlike conventional solutions which only update the user latent features, the recommendation system updates both user latent features and item latent features in a matrix factorization model based on the rating of the item. The updating is performed in real time which enables the recommendation system to quickly adapt to the user ratings to provide new recommendations. In one or more implementations, to update the user latent features and the item latent features in the matrix factorization model, the recommendation system utilizes a Rao-Blackwellized particle filter for online matrix factorization.
    Type: Application
    Filed: October 16, 2015
    Publication date: April 20, 2017
    Inventors: Jaya B. Kawale, Branislav Kveton, Hung H. Bui
  • Publication number: 20160148253
    Abstract: A temporal prediction model is described that is usable to predict user purchase behavior for an online advertising instance. The temporal prediction model may be formed by processing time windows for click data, conversion data, and side information. In one or more implementations, temporal dynamics are applied to the click data, the conversion data, and/or the side information via the processed time windows. Various processing techniques of the temporal prediction model may utilize the applied temporal dynamics to predict user purchase behavior and/or effectiveness of an online advertising instance.
    Type: Application
    Filed: November 25, 2014
    Publication date: May 26, 2016
    Inventors: Jaya B. Kawale, Sheng Li