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.

Abstract: The current subject matter provides systems, methods, computer program products (including graphical user interfaces) in which user interests can be identified by characterizing the intent of the interaction with a data source such as a business intelligence (BI) system. A set of features can be identified for an adequate description of intents, and a similarity measure for grouping intents into coherent interests. Related systems, apparatus, and methods are also disclosed.

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.

Abstract: The current subject matter provides systems, methods, computer program products (including graphical user interfaces) in which user interests can be identified by characterizing the intent of the interaction with a data source such as a business intelligence (BI) system. A set of features can be identified for an adequate description of intents, and a similarity measure for grouping intents into coherent interests. Related systems, apparatus, and methods are also disclosed.