Purchases method and system

• US 7,983,926 B2
• Filed: 06/08/2007
• Issued: 07/19/2011
• Est. Priority Date: 06/08/2007
• Status: Expired due to Fees

• Current Assignee: Conopco (Unilever PLC)
• Original Assignee: Conopco (Unilever PLC)
• Inventors: Li, Ming, Lisboa Gomes, Paulo Jorge, Adjali, Iqbal, Dias, Malcolm Benjamin, Sordo-Garcia, Carmen Maria, El-Deredy, Wael
• Primary Examiner(s): SALIARD, SHANNON S
• Application Number:
  US11/811,175
• Publication Number:
  US 20080306808A1
• Time in Patent Office: 1,502 Days
• Field of Search: 705/1.1
• US Class Current: 705/1.1
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

In an automated method for providing personalised recommendations to a user, a global probabilistic purchase model based on prior interactions of a group of users of a system, is used in the generation of personalised recommendations for future purchases for a given user. Attributes of the given user are used to identify characteristics relating to the user'"'"'s personal purchasing, correction factors are calculated to update the output of the global probabilistic purchase model to personalise the recommendations for the user.

Claims

1. An automated method for providing personalised purchase recommendations to a user, the method carried out on a mobile computing device programmed to carry out the method in combination with a server, said method comprising the steps of:

generating a global probabilistic purchase model on the server of purchases in a general population over an entire range of users, the model providing recommendations for future purchases based on purchase transactions of the general population;

detecting on the mobile device, attributes of the user so as to identify personal characteristics relating to a behavior of the user;

calculating at least one correction factor, based on the detected attributes of the user; and

applying on the mobile device the correction factor to the global probabilistic purchase model to modify the recommendations of said model according to personal preferences of the user thereby generating a personalized recommendation list for future purchases for the user;

wherein the mobile device applies the correction factor to convert an output P(y|X) of the global probabilistic purchase model to an output {circumflex over (P)}(y|X) of a personalized probabilistic model for the user using a personalized prior {circumflex over (P)}(y) of the user wherein {circumflex over (P)}(y|X) is computed either according to Eq (1) which assumes equal global priors,

2. A method according to claim 1 wherein the attributes of the user includes data on prior purchases of the user.

3. A method according to claim 2 wherein the data on the prior purchases of the user incorporates a calculation based on number of previous visits made.

4. A method according to claim 1 wherein attributes of the user includes data on user reports.

5. A method according to claim 4 wherein the data is derived from results of a purchasing questionnaire completed by the user.

6. A method according to claim 4 wherein the data is derived from results of an opinion survey completed by the user.

7. A method according to claim 1 wherein attributes of the user includes behavioural pattern data relating to the users.

8. A method according to claim 1 wherein attributes of the user includes psychological data relating to the users.

9. A method according to claim 7 wherein the behavioural pattern data includes input representing interactions between the user and a device.

10. A method according to claim 1 wherein the personalised recommendations list further includes data on equivalent and/or similar recommendations based on prior user documentation.

11. A method according to claim 10 wherein the data on equivalent and/or similar recommendations comprise a cost-reduction factor.

12. A method according to claim 10 wherein the data on equivalent and/or similar recommendations comprise a quality-increase factor.

13. A method according to claim 1 comprising storing data representing the user attributes and/or the correction factor on the mobile device.

14. A method according to claim 1, comprising storing data relating to the global probabilistic purchase model at a central server location accessible for operation with the at least one correction factor for personalisation of recommendations.

15. An automated system for providing personalised recommendations to a user, the system comprising:

a central unit comprising a General Recommendations Formulator capable of producing a global probabilistic purchase model from a Central Data Resource of purchases in a general population of an entire range of users, said model providing recommendations for future purchases as a function of purchase transactions of the general population;

a module to detect attributes of the user so as to identify characteristics relating to the behavior of the user;

means to calculate at least one correction factor, based on the attributes of the user; and

means to apply the correction factor to the global probabilistic model to modify the recommendations of said model according to personal preferences of the user thereby generating a personalized recommendation list for future purchases for the user,

wherein the means to apply the correction factor is configured to apply the correction factor to convert an output P(y|X) of the global probabilistic purchase model to an output {circumflex over (P)}(y|X) of a personalized probabilistic model for the user using a personalized prior {circumflex over (P)}(y) of the user,

wherein {circumflex over (P)}(y|X) is computed either according to Eq (1) which assumes equal global priors,

16. A system according to claim 15 wherein the behavior is data related to prior purchases of the user.

17. A system according to claim 16 wherein the data on prior purchases of the user includes number of previous visits made.

18. A system according to claim 15 wherein the characteristics relating to the behavior of the user includes data on user reports.

19. A system according to claim 18 wherein the data is derived from results of a purchasing questionnaire completed by the user.

20. A system according to claim 18 wherein the data is derived from results of an opinion survey completed by the user.

21. A system according to claim 15 wherein the characteristics relating to the behavior of the user includes behavioural pattern data relating to the user.

22. A system according to claim 15 wherein characteristics relating to the behavior of the user include psychological measures data relating to the user.

23. A system according to claim 21 wherein the behavioural pattern data or psychological measures data includes input representing interactions between the user and a device.

24. A system according to claim 15 wherein the personalized recommendation list for future purchases for the user includes data on equivalent and/or similar recommendations based on prior user documentation.

25. A system according to claim 24 wherein the data on equivalent and/or similar recommendations comprise a cost-reduction factor.

26. A system according to claim 24 wherein the data on equivalent and/or similar recommendations comprise a quality-increase factor.

27. A system according to claim 15 comprising means to store data representing the user attributes and/or the correction factor at a user unit.