公开(公告)号：US10311367B2

公开(公告)日：2019-06-04

申请号：US14988997

申请日：2016-01-06

Applicant: AT&T Intellectual Property I, L.P. ,  Board of Regents, The University of Texas System

Inventor： Kadangode K. Ramakrishnan ,  Divesh Srivastava ,  Tae Won Cho ,  Yin Zhang

IPC: G06F16/30 ,  G06N7/00 ,  G06F16/48 ,  G06F16/242 ,  G06F16/9032 ,  G11B27/10 ,  H04N21/475 ,  H04N21/466

Abstract: Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.

公开(公告)号：US10341182B2

公开(公告)日：2019-07-02

申请号：US15235434

申请日：2016-08-12

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jia Wang ,  Zihui Ge ,  Ajay Mahimkar ,  Aman Shaikh ,  Jennifer Yates ,  Yin Zhang ,  Joanne Emmons

IPC: G06F15/177 ,  H04L12/24

Abstract: A system and method identify a network upgrade from a data set including a plurality of configuration sessions. The system performs the method by receiving a plurality of configuration sessions. Each of the configuration sessions comprises a plurality of configuration commands. The configuration commands are generated by a same user identifier and within a time threshold. The method further includes identifying one of the configuration sessions as a network upgrade session. The identification is based on a rareness of the configuration session or a skewness of the configuration session.

公开(公告)号：US20160117599A1

公开(公告)日：2016-04-28

申请号：US14988997

申请日：2016-01-06

Applicant: AT&T Intellectual Property I, L.P. ,  Board of Regents, The University of Texas System

Inventor： Kadangode K. Ramakrishnan ,  Divesh Srivastava ,  Tae Won Cho ,  Yin Zhang

IPC: G06N7/00

CPC classification number: G06N7/005 ,  G06F17/30038 ,  G06F17/30412 ,  G06F17/3097 ,  G11B27/105 ,  H04N21/4661 ,  H04N21/4668 ,  H04N21/4756

Abstract: Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.

公开(公告)号：US09262553B2

公开(公告)日：2016-02-16

申请号：US14567113

申请日：2014-12-11

Applicant: AT&T Intellectual Property I, L.P. ,  Board of Regents, The University of Texas System

Inventor： Kadangode K. Ramakrishnan ,  Divesh Srivastava ,  Tae Won Cho ,  Yin Zhang

IPC: G06F17/30 ,  G11B27/10 ,  H04N21/475 ,  H04N21/466

CPC classification number: G06N7/005 ,  G06F17/30038 ,  G06F17/30412 ,  G06F17/3097 ,  G11B27/105 ,  H04N21/4661 ,  H04N21/4668 ,  H04N21/4756

Abstract: Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.

公开(公告)号：US20150100599A1

公开(公告)日：2015-04-09

申请号：US14567113

申请日：2014-12-11

Applicant: AT&T Intellectual Property I, L.P. ,  Board of Regents, The University of Texas System

Inventor： Kadangode K. Ramakrishnan ,  Divesh Srivastava ,  Tae Won Cho ,  Yin Zhang

IPC: G06F17/30

CPC classification number: G06N7/005 ,  G06F17/30038 ,  G06F17/30412 ,  G06F17/3097 ,  G11B27/105 ,  H04N21/4661 ,  H04N21/4668 ,  H04N21/4756

Abstract: Recommendation systems are widely used in Internet applications. In current recommendation systems, users only play a passive role and have limited control over the recommendation generation process. As a result, there is often considerable mismatch between the recommendations made by these systems and the actual user interests, which are fine-grained and constantly evolving. With a user-powered distributed recommendation architecture, individual users can flexibly define fine-grained communities of interest in a declarative fashion and obtain recommendations accurately tailored to their interests by aggregating opinions of users in such communities. By combining a progressive sampling technique with data perturbation methods, the recommendation system is both scalable and privacy-preserving.

Abstract translation: 推荐系统广泛应用于互联网应用。 在目前的推荐系统中，用户只能发挥被动的作用，对推荐生成过程的控制有限。 因此，这些系统提出的建议和实际用户兴趣之间经常存在很大的不匹配，这些建议是细粒度和不断发展的。 通过用户分配的推荐体系结构，个人用户可以灵活地定义精细的社区，并以声明方式定义感兴趣的社区，通过汇总用户在这些社区的意见，获得准确定制的兴趣建议。 通过将逐行采样技术与数据扰动方法相结合，推荐系统既可扩展又保密。