Recommendation system based on group profiles of personal taste

1. A group recommendation system, comprising:

a non-transitory computer-readable medium holding a database comprising characteristics for a plurality of candidate items;

a processor; and

a non-transitory computer-readable medium holding programming instructions that, when executed, are configured instruct the processor to:

identify a preference profile for each user in a group of users, wherein the preference profile for each user comprises data that represents a pattern of dependency between the user's ratings for a plurality of items that the user has rated and characteristics of at least a portion of the rated items to which the user's ratings apply;

merge a plurality of the users' data that represents the patterns of dependency between ratings and characteristics for the plurality of users into a group preference profile wherein the merging comprises:

identifying a plurality of consistent preference models for the users,

merging the identified consistent preference models into a merged preference model, and

including the merged preference model in the group preference profile;

receive a request for a group recommendation;

select, from the database based on the group preference profile, a candidate item having characteristics that at least a plurality of users in group are expected to find appealing; and

generate a recommendation for the selected candidate item.

2. The system of claim 1 , wherein the instructions that are configured instruct the processor to merge a plurality of the users' data into the group preference profile also comprise instructions to:

identify merged item descriptions for a set of the rated items; and

include the merged item descriptions in the group preference profile.

3. The system of claim 2 , wherein the instructions that are configured to instruct the processor to identify the merged item descriptions for the set of rated items comprise instructions to:

identify similar item descriptors in the preference profiles for a plurality of the users; and

merge the similar item descriptors into the merged item description.

4. The system of claim 3 , further comprising instructions that are configured to instruct the processor to require, as a condition of merging the similar item descriptors into the merged item description for an item, that at least a threshold portion of the users have preference profiles that include similar item descriptors for that item.

5. The system of claim 1 , wherein the instructions that are configured instruct the processor to merge a plurality of the users' data into the group preference profile also comprise instructions to:

identify merged degrees of appeal for a plurality of items and classes; and

include the merged degrees of appeal in the group preference profile.

6. The system of claim 5 , wherein the instructions that are configured to instruct the processor to identify the merged degrees of appeal comprise instructions to:

identify an item or class to which a plurality of the users have assigned similar ratings; and

merge the similar ratings into the merged degree of appeal.

7. The system of claim 6 , further comprising instructions that are configured to instruct the processor to require, as a condition of merging the similar ratings into the merged degree of appeal, that at least a threshold portion of the users have assigned similar ratings to that item or class.

8. The system of claim 1 , further comprising instructions that are configured to instruct the processor to determine a confidence level in the group preference profile.

9. The system of claim 1 , further comprising instructions that are configured to instruct the processor to:

determine whether the request for a recommendation comprises a constraint; and

if the request comprises a constraint, require that the candidate item satisfies the constraint before recommending the candidate item.

10. The system of claim 1 , further comprising instructions that are configured to instruct the processor to:

receive a precedence order for the users in the group; and

when developing the group preference profile, assign a higher weight to a pattern of dependency associated with a user who is higher in the precedence order than to a pattern of dependency associated with a user who is lower in the precedence order.

11. The system of claim 1 , further comprising instructions that are configured to:

include a plurality of additional merged preference models in the group preference profile; and

order the merged preference models that are included in the group preference profile on the basis of a social welfare function.

12. A method, comprising;

receiving, via a user interface, a selection of a group having a plurality of members;

by a processor, identifying a preference profile for each member, wherein the preference profile for each member comprises:

data that represents a pattern of dependency between the member's ratings for a plurality of items that the profile's member has rated and characteristics of at least a portion of the rated items to which the member's ratings apply;

by the processor, developing a group preference profile by merging the data that represents the patterns of dependency between ratings and characteristics for each of the members, wherein the merging comprises:

identifying a plurality of consistent preference models for the members, wherein each of the consistent preference models is associated with a positive degree of appeal,

merging the identified consistent preference models into a merged preference model, and

including the merged preference model in the group preference profile;

receiving, via the user interface, a request for a recommendation for an item;

by the processor, accessing a database of candidate items, wherein each candidate item is associated with at least one characteristic;

by the processor, using the group preference profile to select, from the database, a candidate item having characteristics which are likely to appeal to at least a plurality of members of the group; and

by the processor, generating a recommendation for the selected candidate item.

13. The method of claim 12 , further comprising:

determining that the request for a recommendation comprises a constraint; and

when selecting the identified candidate item, confirming that the identified candidate item satisfies the constraint.

14. The method of claim 12 , wherein merging the data that represents a pattern of dependency to develop the group preference profile also comprises:

identifying merged item descriptions for a set of items that have been rated by the members; and

including the merged item descriptions in the group preference profile.

15. The method of claim 14 , wherein identifying the merged item descriptions for the set of rated items comprises:

identifying similar item descriptors in the preference profiles for a plurality of the users; and

merging the similar item descriptors into the merged item description.

16. The method of claim 15 , further comprising, as a condition of merging the similar item descriptors into the merged item description for an item, requiring that at least a threshold portion of the members have preference profiles that include similar item descriptors for that item.

17. The method of claim 12 , wherein merging the data that represents a pattern of dependency to develop the group preference profile also comprises:

identifying merged degrees of appeal for a plurality of items and classes; and

including the merged degrees of appeal in the group preference profile.

18. The method of claim 17 , wherein identifying the merged degrees of appeal comprises:

identifying an item or class to which a plurality of the members have assigned similar ratings; and

merging the similar ratings into the merged degree of appeal.

19. The method of claim 18 , further comprising, as a condition of merging the similar ratings into the merged degree of appeal, requiring that at least a threshold portion of the identified users have assigned similar ratings to that item or class.

20. The method of claim 12 , further comprising determining a confidence level in the group preference profile.

21. The method of claim 12 , further comprising:

receiving a precedence order for the members; and

when developing the group preference profile, assigning a higher weight to a pattern of dependency associated with a member who is higher in the precedence order than to a pattern of dependency associated with a member who is lower in the precedence order.

22. The method of claim 12 , further comprising:

including a plurality of additional merged preference models in the group preference profile; and

ordering the merged preference models that are included in the group preference profile on the basis of a social welfare function.

23. A group recommendation system, comprising:

a non-transitory computer-readable medium holding a database comprising characteristics for a plurality of candidate items;

a processor; and

a non-transitory computer-readable medium holding programming instructions that, when executed, are configured instruct the processor to:

identify a preference profile for each user in a group of users, wherein the preference profile for each user comprises data that represents a pattern of dependency between the user's ratings for a plurality of items that the user has rated and characteristics of at least a portion of the rated items to which the user's ratings apply;

merge a plurality of the users' data that represents the patterns of dependency between ratings and characteristics for the plurality of users into a group preference profile wherein the merging comprises:

identifying merged item descriptions for a set of the rated items by:

identifying similar item descriptors in the preference profiles for a plurality of the users; and

merging the similar item descriptors into the merged item descriptions while requiring, as a condition of merging the similar item descriptors into the merged item description for an item, that at least a threshold portion of the users have preference profiles that include similar item descriptors for that item, and

including the merged item descriptions in the group preference profile;

receive a request for a group recommendation;

select, from the database based on the group preference profile, a candidate item having characteristics that at least a plurality of users in group are expected to find appealing; and

generate a recommendation for the selected candidate item.

24. The system of claim 23 , wherein the instructions that are configured to instruct the processor to merge a plurality of the users' data into the group preference profile also comprise instructions to:

identify merged degrees of appeal for a plurality of items and classes; and

include the merged degrees of appeal in the group preference profile.

25. The system of claim 24 , wherein the instructions that are configured to instruct the processor to identify the merged degrees of appeal comprise instructions to:

identify an item or class to which a plurality of the users have assigned similar ratings; and

merge the similar ratings into the merged degree of appeal.

26. The system of claim 25 , further comprising instructions that are configured to instruct the processor to require, as a condition of merging the similar ratings into the merged degree of appeal, that at least a threshold portion of the users have assigned similar ratings to that item or class.

27. The system of claim 23 , further comprising instructions that are configured to instruct the processor to determine a confidence level in the group preference profile.

28. The system of claim 23 , further comprising instructions that are configured to instruct the processor to:

determine whether the request for a recommendation comprises a constraint; and

if the request comprises a constraint, require that the candidate item satisfies the constraint before recommending the candidate item.

29. The system of claim 23 , further comprising instructions that are configured to instruct the processor to:

receive a precedence order for the users in the group; and

when developing the group preference profile, assign a higher weight to a pattern of dependency associated with a user who is higher in the precedence order than to a pattern of dependency associated with a user who is lower in the precedence order.

30. The system of claim 23 , wherein:

the instructions that are configured instruct the processor to merge a plurality of the users' data into the group preference profile also comprise instructions to:

identify a plurality of consistent preference models for the users,

merge the identified consistent preference models into a merged preference model, and

include the merged preference model in the group preference profile; and

the non-transitory storage medium further comprises additional instructions that are configured to:

include a plurality of additional merged preference models in the group preference profile, and

order the merged preference models that are included in the group preference profile on the basis of a social welfare function.

31. A group recommendation system, comprising:

a non-transitory computer-readable medium holding a database comprising characteristics for a plurality of candidate items;

a processor; and

a non-transitory computer-readable medium holding programming instructions that, when executed, are configured instruct the processor to:

identify a preference profile for each user in a group of users, wherein the preference profile for each user comprises data that represents a pattern of dependency between the user's ratings for a plurality of items that the user has rated and characteristics of at least a portion of the rated items to which the user's ratings apply;

merge a plurality of the users' data that represents the patterns of dependency between ratings and characteristics for the plurality of users into a group preference profile wherein the merging comprises:

identifying merged degrees of appeal for a plurality of items and classes by:

identifying an item or class to which a plurality of the users have assigned similar ratings; and

merging the similar ratings into the merged degree of appeal while requiring, as a condition of merging the similar ratings into the merged degree of appeal, that at least a threshold portion of the users have assigned similar ratings to that item or class, and

including the merged degrees of appeal in the group preference profile;

receive a request for a group recommendation;

select, from the database based on the group preference profile, a candidate item having characteristics that at least a plurality of users in group are expected to find appealing; and

generate a recommendation for the selected candidate item.

32. The system of claim 31 , further comprising instructions that are configured to instruct the processor to determine a confidence level in the group preference profile.

33. The system of claim 31 , further comprising instructions that are configured to instruct the processor to:

determine whether the request for a recommendation comprises a constraint; and

if the request comprises a constraint, require that the candidate item satisfies the constraint before recommending the candidate item.

34. The system of claim 31 , further comprising instructions that are configured to instruct the processor to:

receive a precedence order for the users in the group; and

when developing the group preference profile, assign a higher weight to a pattern of dependency associated with a user who is higher in the precedence order than to a pattern of dependency associated with a user who is lower in the precedence order.

35. The system of claim 31 , wherein:

the instructions that are configured instruct the processor to merge a plurality of the users' data into the group preference profile also comprise instructions to:

identify a plurality of consistent preference models for the users,

merge the identified consistent preference models into a merged preference model, and

include the merged preference model in the group preference profile; and

the non-transitory storage medium further comprises additional instructions that are configured to:

include a plurality of additional merged preference models in the group preference profile, and

order the merged preference models that are included in the group preference profile on the basis of a social welfare function.

36. A method, comprising:

receiving, via a user interface, a selection of a group having a plurality of members;

by a processor, identifying a preference profile for each member, wherein the preference profile for each member comprises data that represents a pattern of dependency between the member's ratings for a plurality of items that the profile's member has rated and characteristics of at least a portion of the rated items to which the member's ratings apply;

by the processor, developing a group preference profile by merging the data that represents the patterns of dependency between ratings and characteristics for each of the members, wherein the merging comprises:

identifying merged item descriptions for a set of items that have been rated by the members by:

identifying similar item descriptors in the preference profiles for a plurality of the members; and

merging the similar item descriptors into the merged item description while requiring, as a condition of merging the similar item descriptors into the merged item description for an item, that at least a threshold portion of the members have preference profiles that include similar item descriptors for that item, and

including the merged item descriptions in the group preference profile;

receiving, via the user interface, a request for a recommendation for an item;

by the processor, accessing a database of candidate items, wherein each candidate item is associated with at least one characteristic;

by the processor, using the group preference profile to select, from the database, a candidate item having characteristics which are likely to appeal to at least a plurality of members of the group; and

by the processor, generating a recommendation for the selected candidate item.

37. The method of claim 36 , further comprising:

determining that the request for a recommendation comprises a constraint; and

when selecting the identified candidate item, confirming that the identified candidate item satisfies the constraint.

38. The method of claim 36 , wherein merging the data that represents a pattern of dependency to develop the group preference profile comprises:

identifying merged degrees of appeal for a plurality of items and classes; and

including the merged degrees of appeal in the group preference profile.

39. The method of claim 38 , wherein identifying each of the merged degrees of appeal comprises:

identifying an item or class to which a plurality of the members have assigned similar ratings; and

merging the similar ratings into the merged degree of appeal.

40. The method of claim 39 , further comprising, as a condition of merging the similar ratings into the merged degree of appeal, requiring that at least a threshold portion of the identified members have assigned similar ratings to that item or class.

41. The method of claim 36 , further comprising determining a confidence level in the group preference profile.

42. The method of claim 36 , further comprising:

receiving a precedence order for the members; and

when developing the group preference profile, assigning a higher weight to a pattern of dependency associated with a member who is higher in the precedence order than to a pattern of dependency associated with a member who is lower in the precedence order.

43. The method of claim 36 , wherein:

merging the data that represents a pattern of dependency to develop the group preference profile also comprises:

identifying a plurality of consistent preference models for the members, wherein each of the consistent preference models is associated with a positive degree of appeal,

merging the identified consistent preference models into a merged preference model, and

including the merged preference model in the group preference profile; and the method further comprises:

including a plurality of additional merged preference models in the group preference profile, and

ordering the merged preference models that are included in the group preference profile on the basis of a social welfare function.

44. A method, comprising:

receiving, via a user interface, a selection of a group having a plurality of members;

by a processor, identifying a preference profile for each member, wherein the preference profile for each member comprises data that represents a pattern of dependency between the member's ratings for a plurality of items that the profile's member has rated and characteristics of at least a portion of the rated items to which the member's ratings apply;

by the processor, developing a group preference profile by merging the data that represents the patterns of dependency between ratings and characteristics for each of the members, wherein the merging comprises:

identifying merged degrees of appeal for a plurality of items and classes by:

identifying an item or class to which a plurality of the members have assigned similar ratings, and

merging the similar ratings into the merged degree of appeal, while requiring, as a condition of merging the similar ratings into the merged degree of appeal, requiring that at least a threshold portion of the identified members have assigned similar ratings to that item or class, and

including the merged degrees of appeal in the group preference profile;

receiving, via the user interface, a request for a recommendation for an item;

by the processor, accessing a database of candidate items, wherein each candidate item is associated with at least one characteristic;

by the processor, using the group preference profile to select, from a database, a candidate item having characteristics which are likely to appeal to at least a plurality of members of the group; and

by the processor, generating a recommendation for the selected candidate item.

45. The method of claim 44 , further comprising determining a confidence level in the group preference profile.

46. The method of claim 44 , further comprising:

receiving a precedence order for the members; and

when developing the group preference profile, assigning a higher weight to a pattern of dependency associated with a member who is higher in the precedence order than to a pattern of dependency associated with a member who is lower in the precedence order.

47. The method of claim 44 , wherein:

merging the data that represents a pattern of dependency to develop the group preference profile also comprises:

identifying a plurality of consistent preference models for the members, wherein each of the consistent preference models is associated with a positive degree of appeal,

merging the identified consistent preference models into a merged preference model, and

including the merged preference model in the group preference profile; and the method further comprises:

including a plurality of additional merged preference models in the group preference profile, and

ordering the merged preference models that are included in the group preference profile on the basis of a social welfare function.