Patents by Inventor Matteo Sesia

Matteo Sesia 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).

  • Recommendation system using linear stochastic bandits and confidence interval generation
    Patent number: 11100559
    Abstract: Recommendation systems and techniques are described that use linear stochastic bandits and confidence interval generation to generate recommendations for digital content. These techniques overcome the limitations of conventional recommendations systems that are limited to a fixed parameter to estimate noise and thus do not fully exploit available data and are overly conservative, at a significant cost in operational performance of a computing device. To do so, a linear model, noise estimate, and confidence interval are refined by a recommendation system based on user interaction data that describes a result of user interaction with items of digital content. This is performed by comparing a result of the recommendation on user interaction with digital content with an estimate of a result of the recommendation.
    Type: Grant
    Filed: March 29, 2018
    Date of Patent: August 24, 2021
    Assignee: Adobe Inc.
    Inventors: Matteo Sesia, Yasin Abbasi Yadkori
  • Recommendation System using Linear Stochastic Bandits and Confidence Interval Generation
    Publication number: 20190303994
    Abstract: Recommendation systems and techniques are described that use linear stochastic bandits and confidence interval generation to generate recommendations for digital content. These techniques overcome the limitations of conventional recommendations systems that are limited to a fixed parameter to estimate noise and thus do not fully exploit available data and are overly conservative, at a significant cost in operational performance of a computing device. To do so, a linear model, noise estimate, and confidence interval are refined by a recommendation system based on user interaction data that describes a result of user interaction with items of digital content. This is performed by comparing a result of the recommendation on user interaction with digital content with an estimate of a result of the recommendation.
    Type: Application
    Filed: March 29, 2018
    Publication date: October 3, 2019
    Applicant: Adobe Inc.
    Inventors: Matteo Sesia, Yasin Abbasi Yadkori