公开(公告)号：US20190163782A1

公开(公告)日：2019-05-30

申请号：US15964814

申请日：2018-04-27

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Krista Drushku ,  Nicolas Labroche ,  Patrick Marcel ,  Verónika Peralta

IPC: G06F17/30 ,  G06Q10/06 ,  H04L29/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving recommendation to users in data intelligence systems. In one aspect, a method includes the actions of receiving a current observation describing an interaction of a user with a data intelligence system; identifying a current user interest based on the current observation, wherein past observations of the user with the data intelligence system are clustered to form user interests in a Markov model; using the Markov model and based on the current user interest, determining a next user interest from the user interests; extracting a one past observation from the determined next user interest based on a selection criterion and a threshold, wherein the selection criterion is based on how closely the at least one past observation matches the current observation; and sending a recommendation to the user based on the past observation.

公开(公告)号：US10915522B2

公开(公告)日：2021-02-09

申请号：US15964814

申请日：2018-04-27

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Krista Drushku ,  Nicolas Labroche ,  Patrick Marcel ,  Verónika Peralta

IPC: G06F16/242 ,  G06F16/335 ,  H04L29/08 ,  G06Q10/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving recommendation to users in data intelligence systems. In one aspect, a method includes the actions of receiving a current observation describing an interaction of a user with a data intelligence system; identifying a current user interest based on the current observation, wherein past observations of the user with the data intelligence system are clustered to form user interests in a Markov model; using the Markov model and based on the current user interest, determining a next user interest from the user interests; extracting a one past observation from the determined next user interest based on a selection criterion and a threshold, wherein the selection criterion is based on how closely the at least one past observation matches the current observation; and sending a recommendation to the user based on the past observation.