Concerted learning and multi-instance sequential prediction tree
A method, system and computer readable medium for performing a purchase prediction operation. The purchase prediction operation includes: selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser; capturing a product term associated with a most recent purchase period of the target purchaser; performing a sequential recommendation operation, the sequential recommendation operation providing a sequence recommendation score; and, generating a purchase pattern prediction for the target user based upon the sequential recommendation score.
1. A computer-implementable method for performing a purchase prediction operation, comprising:
selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser;
capturing a product term associated with a most recent purchase period of the target purchaser;
performing a sequential recommendation operation, the sequential recommendation operation providing a sequential recommendation score, the sequential recommendation score being based upon a number of historical purchase periods, a number of future purchase periods, and a similarity quotient score, the similarity quotient score representing a degree of similarity between historical purchase behavior of the target purchaser and historical purchase behavior of at least one neighbor purchaser, the-neighbor purchaser comprising a purchaser in a set of purchasers, the set of purchasers including the target purchaser and another neighbor purchaser;
using the similarity quotient score to select neighbor purchasers with similar historical purchase behavior; and,
generating a purchase pattern prediction for the target purchaser based upon the sequential recommendation score and the neighbor purchasers with similar historical purchase behavior.
2. The method of claim 1 , wherein:
the sequential recommendation operation comprises a concerted learning component (CLC) and a multi-instance sequential component (MISC).
3. The method of claim 1 , wherein:
the sequential recommendation operation uses historical purchase patterns associated with similar customers to provide predictions of future purchases for the purchaser.
4. The method of claim 1 , wherein:
the sequential recommendation operation is implemented to search for most recent list of products (MRLP) purchased by a target customer during a most recent purchase period.
5. The method of claim 4 , wherein:
searching for the MRLP is performed for similar customers, the result of searching for the MRLP for similar customers being used to generate a prediction tree, the prediction tree being based on a multi-instance occurrence of MRLP.
6. The method of claim 5 , wherein:
the prediction tree is implemented to capture multiple sets of ‘n’ purchase sequences (n-PS) after the MRLP; and,
MISC scores are computed for each purchase sequence n-PS.
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:
selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser;
capturing a product term associated with a most recent purchase period of the target purchaser;
performing a sequential recommendation operation, the sequential recommendation operation providing a sequential recommendation score, the sequential recommendation score being based upon a number of historical purchase periods, a number of future purchase periods, and a similarity quotient score, the similarity quotient score representing a degree of similarity between historical purchase behavior of the target purchaser and historical purchase behavior of at least one neighbor purchaser, the-neighbor purchaser comprising a purchaser in a set of purchasers, the set of purchasers including the target purchaser and another neighbor purchaser;
using the similarity quotient score to select neighbor purchasers with similar historical purchase behavior; and,
generating a purchase pattern prediction for the target purchaser based upon the sequential recommendation score and the neighbor purchasers with similar historical purchase behavior.
8. The system of claim 7 , wherein:
the sequential recommendation operation comprises a concerted learning component (CLC) and a multi-instance sequential component (MISC).
9. The system of claim 7 , wherein:
the sequential recommendation operation uses historical purchase patterns associated with similar customers to provide predictions of future purchases for the purchaser.
10. The system of claim 9 , wherein:
the sequential recommendation operation is implemented to search for most recent list of products (MRLP) purchased by a target customer during a most recent purchase period.
11. The system of claim 10 , wherein the instructions executable by the processor are further configured for:
searching for the MRLP is performed for similar customers, the result of searching for the MRLP for similar customers being used to generate a prediction tree, the prediction tree being based on a multi-instance occurrence of MRLP.
12. The system of claim 11 , wherein:
the prediction tree is implemented to capture multiple sets of ‘n’ purchase sequences (n-PS) after the MRLP; and,
MISC scores are computed for each purchase sequence n-PS.
13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser;
capturing a product term associated with a most recent purchase period of the target purchaser;
performing a sequential recommendation operation, the sequential recommendation operation providing a sequential recommendation score, the sequential recommendation score being based upon a number of historical purchase periods, a number of future purchase periods, and a similarity quotient score, the similarity quotient score representing a degree of similarity between historical purchase behavior of the target purchaser and historical purchase behavior of at least one neighbor purchaser, the-neighbor purchaser comprising a purchaser in a set of purchasers, the set of purchasers including the target purchaser and another neighbor purchaser;
using the similarity quotient score to select neighbor purchasers with similar historical purchase behavior; and,
generating a purchase pattern prediction for the target purchaser based upon the sequential recommendation score and the neighbor purchasers with similar historical purchase behavior.
14. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the sequential recommendation operation comprises a concerted learning component (CLC) and a multi-instance sequential component (MISC).
15. The non-transitory, computer-readable storage medium of claim 13 , wherein:
the sequential recommendation operation uses historical purchase patterns associated with similar customers to provide predictions of future purchases for the purchaser.
16. The non-transitory, computer-readable storage medium of claim 15 , wherein:
the sequential recommendation operation is implemented to search for most recent list of products (MRLP) purchased by a target customer during a most recent purchase period.
17. The non-transitory, computer-readable storage medium of claim 16 , wherein the computer executable instructions are further configured for:
searching for the MRLP is performed for similar customers, the result of searching for the MRLP for similar customers being used to generate a prediction tree, the prediction tree being based on a multi-instance occurrence of MRLP.
18. The non-transitory, computer-readable storage medium of claim 17 , wherein:
the prediction tree is implemented to capture multiple sets of ‘n’ purchase sequences (n-PS) after the MRLP; and,
MISC scores are computed for each purchase sequence n-PS.
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.