Ranked insight machine learning operation
A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a ranked insight machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the ranked insight machine learning operations; and, generating a cognitive insight based upon the cognitive profile generated using the plurality of machine learning operations.
1. A computer-implementable method for generating a cognitive insight comprising:
receiving training data, the training data being based upon interactions between a user and a cognitive inference and learning system;
performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;
performing a ranked insight machine learning operation on the training data, the machine learning operation comprising the ranked insight machine learning operation, the ranked insight machine learning operation generating a set of ranked cognitive insights; and,
providing the set of ranked insights generated via the ranked insight machine learning operation to the user.
2. The method of claim 1 , wherein:
the ranked insight machine learning operation performs a factor-needs operation when generating the set of ranked cognitive insights.
3. The method of claim 2 , wherein:
the factor-needs operation generates a needs graph model, the needs graph model utilizing a matrix approach to map each of a plurality of users to a plurality of factors, each of the plurality of factors comprising certain attributes.
4. The method of claim 3 , wherein:
the factor-needs operation receives information relating to each of a plurality of topics, the information relating to each of the plurality of topics comprising information regarding a degree of abstraction associated with each of a plurality of topics.
5. The method of claim 4 , wherein:
the factor-needs operation determines a highest level of abstraction relevant to each of the plurality of topics.
6. The method of claim 5 , further comprising:
using the highest level of abstraction relevant to each of the plurality of topics to generate a cognitive insight from the set of cognitive insights.
7. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving training data, the training data being based upon interactions between a user and a cognitive inference and learning system;
performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;
performing a ranked insight machine learning operation on the training data, the machine learning operation comprising the ranked insight machine learning operation, the ranked insight machine learning operation generating a set of ranked cognitive insights; and,
providing the set of ranked insights generated via the ranked insight machine learning operation to the user.
8. The system of claim 7 , wherein:
the ranked insight machine learning operation performs a factor-needs operation when generating the set of ranked cognitive insights.
9. The system of claim 8 , wherein:
the factor-needs operation generates a needs graph model, the needs graph model utilizing a matrix approach to map each of a plurality of users to a plurality of factors, each of the plurality of factors comprising certain attributes.
10. The system of claim 9 , wherein:
the factor-needs operation receives information relating to each of a plurality of topics, the information relating to each of the plurality of topics comprising information regarding a degree of abstraction associated with each of a plurality of topics.
11. The system of claim 10 , wherein:
the factor-needs operation determines a highest level of abstraction relevant to each of the plurality of topics.
12. The system of claim 11 , wherein the instructions executable by the processor further comprise instructions for:
using the highest level of abstraction relevant to each of the plurality of topics to generate a cognitive insight from the set of cognitive insights.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving training data, the training data being based upon interactions between a user and a cognitive inference and learning system;
performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;
performing a ranked insight machine learning operation on the training data, the machine learning operation comprising the ranked insight machine learning operation, the ranked insight machine learning operation generating a set of ranked cognitive insights; and,
providing the set of ranked insights generated via the ranked insight machine learning operation to the user.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the ranked insight machine learning operation performs a factor-needs operation when generating the set of ranked cognitive insights.
15. The non-transitory, computer-readable storage medium of claim 14 , wherein:
the factor-needs operation generates a needs graph model, the needs graph model utilizing a matrix approach to map each of a plurality of users to a plurality of factors, each of the plurality of factors comprising certain attributes.
16. The non-transitory, computer-readable storage medium of claim 15 , wherein:
the factor-needs operation receives information relating to each of a plurality of topics, the information relating to each of the plurality of topics comprising information regarding a degree of abstraction associated with each of a plurality of topics.
17. The non-transitory, computer-readable storage medium of claim 16 , wherein:
the factor-needs operation determines a highest level of abstraction relevant to each of the plurality of topics.
18. The non-transitory, computer-readable storage medium of claim 17 , wherein the instructions executable by the processor further comprise instructions for:
using the highest level of abstraction relevant to each of the plurality of topics to generate a cognitive insight from the set of cognitive insights.
19. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are deployable to a client system from a server system at a remote location.
20. The non-transitory, computer-readable storage medium of claim 13 , wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.