Learning user interests for recommendations in business intelligence interactions

One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

• receiving a current observation describing an interaction of a user with a data intelligence system;
• identifying a current user interest from a plurality of user interests based on the current observation, wherein past observations of the user with the data intelligence system are clustered to form the 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 at least 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 at least one past observation.

A system, comprising:

• one or more processors;
• and
• a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a current observation describing an interaction of a user with a data intelligence system;
• identifying a current user interest from a plurality of user interests based on the current observation, wherein past observations of the user with the data intelligence system are clustered to form the 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 at least 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 at least one past observation.