Profile information recommendation method, program and apparatus

• US 20030177110A1
• Filed: 10/07/2002
• Published: 09/18/2003
• Est. Priority Date: 03/15/2002
• Status: Abandoned Application

• Current Assignee: Fujitsu Limited
• Original Assignee: Fujitsu Limited
• Inventors: Ozaki, Toru, Sato, Akira, Ando, Takahisa, Okamoto, Seishi, Inakoshi, Hiroya
• Application Number:
  US10/265,327
• Publication Number:
  US 20030177110A1
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

When a request for processing is made after at least profile data which serves as input and profile attribute to be output have been specified, the case database, which stores case data in which a relation taking place between a plurality of piece of profile data is represented as a set of profile data, is retrieved in the case retrieval step for cases similar to profile data given as input. Next, in the dynamic learning step, significance of each of the attribute values for the attribute specified as output is calculated such that high significance is given to the attribute value which is characteristic of the set of case data retrieved in the case retrieval step. Further, in the recommended data determination step, the score of each piece of profile data in the profile database is calculated based on the significance, as calculated in the dynamic learning step, of each of the attribute values for the attribute specified as output, and profile data with high scores is recommended.

Claims

1. A profile information recommendation method using a profile database and a case database, the profile database storing therein contents, services, users and the like as profile data featured as sets of pairs of an attribute and an attribute value, the case database storing therein relations taking place between a plurality of pieces of profile data as case data represented in the form of profile data, said method comprising:

an input step which specifies and enters at least profile data as input and a profile attribute to be output;

a case retrieval step which retrieves cases similar to profile data given as input, from said case database;

a dynamic learning step which figures out significance of each of attribute values for said attribute specified as output such that high significance is given to said attribute value which is characteristic of a set of cases retrieved in said case retrieval step; and

a recommended data determination step which based on said significance, as figured out in said dynamic learning step, of each of said attribute values for said attribute specified as output, figures out the score of each piece of profile data in said profile database and recommends profile data with high scores.

2. A method as defined in claim 1, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of probability of occurrence of said attribute value in said set of similar cases retrieved in said case retrieval step.

3. A method as defined in claim 1, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of a residual between probability of occurrence of said attribute value in said set of cases for said attribute specified as output in said case database and probability of occurrence of the attribute value in said set of similar cases retrieved in said case retrieval step.

4. A method as defined in claim 1, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of a value corresponding to each attribute value of negative entropy for an attribute value distribution for said attribute value specified as output in said set of cases in said case database pertaining to an occurrence distribution of attribute values for said attribute specified as output in said set of similar cases retrieved in said case retrieval step.

5. A method as defined in claim 1, further comprising:

a reason for recommendation assignment step which selects attribute values with high significance as figured out in said dynamic learning step, in the form of attribute values for reasons for recommendation of said profile data, from among attribute values occurring in each piece of profile data recommended in said recommended data determination step, said reason for recommendation assignment step assigning information on said selected attribute value to said profile data to make recommendations.

6. A method as defined in claim 1, further comprising:

a viewpoint-by-viewpoint recommendation step which selects an attribute with greatest freedom in said set of cases retrieved in said retrieval step for each of said attributes specified as outputs, said viewpoint-by-viewpoint recommendation step recommending profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said attribute selected.

7. A method as defined in claim 6, wherein

said viewpoint-by-viewpoint recommendation step figures out a variance of probabilities of occurrence of attribute values for each of said attributes specified as outputs in said set of cases retrieved in said case retrieval step, selects an attribute with the smallest variance as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said selected attribute.

8. A method as defined in claim 6, wherein

said viewpoint-by-viewpoint recommendation step figures out the sum square of a residual between probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of cases in the case database and probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of similar cases retrieved in said case retrieval step, selects an attribute with the smallest residual sum square as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as calculated in said recommended data determination step, for each of said attribute values for said selected attribute.

9. A method as defined in claim 6, wherein

said viewpoint-by-viewpoint recommendation step figures out Kullback-Leibler'"'"'s amount of information of an attribute value distribution for said attributes specified as outputs in said set of cases in said case database pertaining to said attribute value distribution for said attributes in said set of similar cases retrieved in said case retrieval step for each of said attributes specified as outputs, selects an attribute with the smallest Kullback-Leibler'"'"'s amount of information as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said selected attribute.

10. A method as defined in claim 1, wherein

said case retrieval step retrieves cases similar to profile data given as input, creates a list of attribute values for said attribute occurring in said similar cases and specified as base attribute and retrieves said case database again for similar cases in which said base attribute values included in said list occur.

11. A method as defined in claim 1, further comprising:

an input conversion step which converts input information by applying rules in an input conversion rule base which stores rules for conversion of input information.

12. A method as defined in claim 1, further comprising:

an output conversion step which converts output results by applying rules in an output conversion rule base which stores rules for conversion of output information.

13. A program for recommending profile information, said program allowing a computer to execute:

an input step which specifies and enters at least profile data as input and a profile attribute to be output;

a case retrieval step which retrieves cases similar to profile data given as input, from said case database;

a dynamic learning step which figures out significance of each of attribute values for said attribute specified as output such that high significance is given to said attribute value which is characteristic of a set of cases retrieved in said case retrieval step; and

a recommended data determination step which based on said significance, as figured out in said dynamic learning step, of each of said attribute values for said attribute specified as output, figures out the score of each piece of profile data in said profile database and recommends profile data with high scores.

14. A program as defined in claim 13, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of probability of occurrence of said attribute value in said set of similar cases retrieved in said case retrieval step.

15. A program as defined in claim 13, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of a residual between probability of occurrence of said attribute value in said set of cases for said attribute specified as output in said case database and probability of occurrence of the attribute value in said set of similar cases retrieved in said case retrieval step.

16. A program as defined in claim 13, wherein

said dynamic learning step figures out said significance of each of said attribute values for said attribute specified as output, in the form of a value corresponding to each attribute value of negative entropy for an attribute value distribution for said attribute value specified as output in said set of cases in said case database pertaining to an occurrence distribution of attribute values for said attribute specified as output in said set of similar cases retrieved in said case retrieval step.

17. A program as defined in claim 13, wherein

said program allows said computer to further execute:

a reason for recommendation assignment step which selects attribute values with high significance as figured out in said dynamic learning step, in the form of attribute values for reasons for recommendation of said profile data, from among attribute values occurring in each piece of profile data recommended in said recommended data determination step, said reason for recommendation assignment step assigning information on said selected attribute value to said profile data to make recommendations.

18. A program as defined in claim 13, wherein

said program allows said computer to further execute:

a viewpoint-by-viewpoint recommendation step which selects an attribute with greatest freedom in said set of cases retrieved in said retrieval step for each of said attributes specified as outputs, said viewpoint-by-viewpoint recommendation step recommending profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said attribute selected.

19. A program as defined in claim 18, wherein

said viewpoint-by-viewpoint recommendation step figures out a variance of probabilities of occurrence of attribute values for each of said attributes specified as outputs in said set of cases retrieved in said case retrieval step, selects an attribute with the smallest variance as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said selected attribute.

20. A program as defined in claim 18, wherein

said viewpoint-by-viewpoint recommendation step figures out the sum square of a residual between probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of cases in the case database and probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of similar cases retrieved in said case retrieval step, selects an attribute with the smallest residual sum square as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as calculated in said recommended data determination step, for each of said attribute values for said selected attribute.

21. A program as defined in claim 18, wherein

said viewpoint-by-viewpoint recommendation step figures out Kullback-Leibler'"'"'s amount of information of an attribute value distribution for said attributes specified as outputs in said set of cases in said case database pertaining to said attribute value distribution for said attributes in said set of similar cases retrieved in said case retrieval step for each of said attributes specified as outputs, selects an attribute with the smallest Kullback-Leibler'"'"'s amount of information as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out in said recommended data determination step, for each of said attribute values for said selected attribute.

22. A program as defined in claim 13, wherein

said case retrieval step retrieves cases similar to profile data given as input, creates a list of attribute values for said attribute occurring in said similar cases and specified as base attribute and retrieves said case database again for similar cases in which said base attribute values included in said list occur.

23. A program as defined in claim 13, wherein

said program allows said computer to further execute:

an input conversion step which converts input information by applying rules in an input conversion rule base which stores rules for conversion of input information.

24. A program as defined in claim 13, wherein

said program allows said computer to further execute:

an output conversion step which converts output results by applying rules in an output conversion rule base which stores rules for conversion of output information.

25. A profile information recommendation apparatus comprising:

a profile database which stores therein contents, services, users and the like as profile data featured as sets of pairs of an attribute and an attribute value;

a case database which stores therein relations taking place between a plurality of pieces of profile data as case data represented in the form of sets of profile data;

a case retrieval unit which retrieves cases similar to profile data given as input, from said case database;

a dynamic learning unit which figures out significance of each of attribute values for said attribute specified as output such that high significance is given to said attribute value which is characteristic of a set of cases retrieved by said case retrieval unit; and

a recommended data determination unit which based on said significance, as figured out by said dynamic learning unit, of each of said attribute values for said attribute specified as output, figures out the score of each piece of profile data in said profile database and recommends profile data with high scores.

26. An apparatus as defined in claim 25, wherein

said dynamic learning unit figures out said significance of each of said attribute values for said attribute specified as output, in the form of probability of occurrence of said attribute value in said set of similar cases retrieved by said case retrieval unit.

27. An apparatus as defined in claim 25, wherein

said dynamic learning unit figures out said significance of each of said attribute values for said attribute specified as output, in the form of a residual between probability of occurrence of said attribute value in said set of cases for said attribute specified as output in said case database and probability of occurrence of the attribute value in said set of similar cases retrieved by said case retrieval unit.

28. An apparatus as defined in claim 25, wherein

said dynamic learning unit figures out said significance of each of said attribute values for said attribute specified as output, in the form of a value corresponding to each attribute value of negative entropy for an attribute value distribution for said attribute value specified as output in said set of cases in said case database pertaining to an occurrence distribution of attribute values for said attribute specified as output in said set of similar cases retrieved by said case retrieval unit.

29. An apparatus as defined in claim 25, further comprising:

a reason for recommendation assignment unit which selects attribute values with high significance as figured out by said dynamic learning unit, in the form of attribute values for reasons for recommendation of said profile data, from among attribute values occurring in each piece of profile data recommended by said recommended data determination unit, said reason for recommendation assignment unit assigning information on said selected attribute value to said profile data to make recommendations.

30. An apparatus as defined in claim 25, further comprising:

a viewpoint-by-viewpoint recommendation unit which selects an attribute with greatest freedom in said set of cases retrieved by said retrieval unit for each of said attributes specified as outputs, said viewpoint-by-viewpoint recommendation unit recommending profile data with high scores, as figured out by said recommended data determination unit, for each of said attribute values for said attribute selected.

31. An apparatus as defined in claim 30, wherein

said viewpoint-by-viewpoint recommendation unit figures out a variance of probabilities of occurrence of attribute values for each of said attributes specified as outputs in said set of cases retrieved by said case retrieval unit, selects an attribute with the smallest variance as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out by said recommended data determination unit, for each of said attribute values for said selected attribute.

32. An apparatus as defined in claim 30, wherein

said viewpoint-by-viewpoint recommendation unit figures out the sum square of a residual between probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of cases in the case database and probability of occurrence of each of said attribute values for each of said attributes specified as outputs in said set of similar cases retrieved by said case retrieval unit, selects an attribute with the smallest residual sum square as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as calculated by said recommended data determination unit, for each of said attribute values for said selected attribute.

33. An apparatus as defined in claim 30, wherein

said viewpoint-by-viewpoint recommendation unit figures out Kullback-Leibler'"'"'s amount of information of an attribute value distribution for said attributes specified as outputs in said set of cases in said case database pertaining to said attribute value distribution for said attributes in said set of similar cases retrieved by said case retrieval unit for each of said attributes specified as outputs, selects an attribute with the smallest Kullback-Leibler'"'"'s amount of information as said attribute with the greatest freedom for recommendation and recommends profile data with high scores, as figured out by said recommended data determination unit, for each of said attribute values for said selected attribute.

34. An apparatus as defined in claim 25, wherein

said case retrieval unit retrieves cases similar to profile data given as input, creates a list of attribute values for said attribute occurring in said similar cases and specified as base attribute and retrieves said case database again for similar cases in which said base attribute values included in said list occur.

35. An apparatus as defined in claim 25, further comprising:

an input conversion rule base which stores therein rules for conversion of input information; and

an input conversion unit which converts input information by applying rules in said input conversion rule base.

36. An apparatus as defined in claim 25, further comprising:

an output conversion unit which converts output results by applying rules in an output conversion rule base which stores rules for conversion of output information.