Mixed-model recommender for leisure activities

• US 7,743,067 B2
• Filed: 09/18/2007
• Issued: 06/22/2010
• Est. Priority Date: 09/18/2007
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Ducheneaut, Nicholas B., Partridge, Kurt E., Price, Robert R.
• Primary Examiner(s): Ehichioya; Fred I
• Application Number:
  US11/856,913
• Publication Number:
  US 20090077057A1
• Time in Patent Office: 1,008 Days
• Field of Search: 707/769, 707/999.001, 707/999.003, 707/999.1, 707/999.2
• US Class Current: 707/769
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

One embodiment of the present invention provides a method for recommending leisure activities to a user. During operation, the system receives at least one query for leisure activities. The system then determines a collaborative filtering score of a candidate activity based on a collaborative filtering model, a soft query score for the candidate activity based on a soft query model, a content preference score for the candidate activity based on a content preference model and the user'"'"'s past behavior, and a distance score for the candidate activity based on a distance model. Next, the system generates a composite score for the candidate activity by calculating a weighted average of the collaborative filtering score, the soft query score, the content preference score, and the distance score. The system further returns a recommendation list containing the activities with the highest composite scores.

Claims

1. A computer-implemented method for recommending leisure activities to a user, the method comprising:

receiving one query for leisure activities, which are associated with a plurality of activity types;

determining by a computer a collaborative rating of a candidate activity by other users;

determining by the computer the user'"'"'s preference for the candidate activity based on a soft query on a set of attributes associated with the candidate activity;

determining by the computer the user'"'"'s interest in the candidate activity based on the user'"'"'s past behavior;

determining by the computer proximity of the candidate activity to the user'"'"'s current location;

generating by the computer a composite score for the candidate activity based on the type of the candidate activity and a combination of the collaborative rating, the user'"'"'s preference, the user'"'"'s interest, and the proximity of the candidate activity; and

recommending to the user a list of leisure activities of different activity types based on composite scores of the leisure activities.

2. The method of claim 1, wherein determining the collaborative rating comprises receiving a set of user-profiling information.

3. The method of claim 1, wherein determining the collaborative rating comprises retrieving one or more ratings for the candidate activity submitted by other users in a same profile group to which the user is matched.

4. The method of claim 1, wherein determining the user'"'"'s preference comprises receiving preference information from the user.

5. The method of claim 1, wherein determining the user'"'"'s interest comprises:

extracting a set of keywords from a set of content previously accessed by the user; and

determining whether a description of the candidate activity matches any of the keywords.

6. The method of claim 1, wherein determining the proximity comprises computing a distance between a location associated with the candidate activity and a location associated with the user.

7. The method of claim 6, wherein determining the proximity further comprises:

receiving a set of GPS coordinates; and

computing a motion range for the user.

8. A computer system for recommending leisure activities to a user, the computer system comprising:

a processor;

a memory;

a receiving mechanism configured to receive at least one query for leisure activities, which are associated with a plurality of activity types;

a collaborative rating mechanism configured to determine a collaborative rating of a candidate activity by other users;

a preference determination mechanism configured to determine the user'"'"'s preference for the candidate activity based on a soft query on a set of attributes associated with the candidate activity;

an interest determination mechanism configured to determine the user'"'"'s interest for the candidate activity based on the user'"'"'s past behavior;

a proximity determination mechanism configured to determine proximity of the candidate activity to the user'"'"'s current location;

a composite scoring mechanism configured to generate a composite score for the candidate activity based on the type of the candidate activity and a combination of the collaborative rating, the user'"'"'s preference, the user'"'"'s interest, and the proximity of the candidate activity; and

a recommendation mechanism configured to recommend a list of leisure activities of different activity types based on composite scores of the leisure activities.

9. The computer system of claim 8, wherein while determining the collaborative rating, the collaborative rating mechanism is further configured to receive a set of user-profiling information.

10. The computer system of claim 8, wherein while determining the collaborative rating, the collaborative rating mechanism is further configured to retrieve one or more ratings for the candidate activity submitted by other users in a same profile group to which the user is matched.

11. The computer system of claim 8, wherein while determining the user'"'"'s preference, the preference determination mechanism is configured to receive preference information from the user.

12. The computer system of claim 8, wherein while determining the user'"'"'s interest, the interest determination mechanism is further configured to:

extract a set of keywords from a set of content previously accessed by the user; and

determine whether a description of the candidate activity matches any of the keywords.

13. The computer system of claim 8, wherein while determining the proximity, the proximity determination mechanism is further configured to compute a distance between a location associated with the candidate activity and a location associated with the user.

14. The computer system of claim 13, wherein while determining the proximity, the proximity determination mechanism is further configured to:

receive a set of GPS coordinates; and

compute a motion range for the user.

15. A computer readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for recommending leisure activities to a user, the method comprising:

receiving one query for leisure activities, which are associated with a plurality of activity types;

determining by a computer a collaborative rating of a candidate activity by other users;

determining by the computer the user'"'"'s preference for the candidate activity based on a soft query on a set of attributes associated with the candidate activity;

determining by the computer the user'"'"'s interest in the candidate activity based on the user'"'"'s past behavior;

determining by the computer proximity of the candidate activity to the user'"'"'s current location;

generating by the computer a composite score for the candidate activity based on the type of the candidate activity and a combination of the collaborative rating, the user'"'"'s preference, the user'"'"'s interest, and the proximity of the candidate activity; and

recommending to the user a list of leisure activities of different activity types based on composite scores of the leisure activities.

16. The computer readable storage medium of claim 15, wherein determining the collaborative rating comprises receiving a set of user-profiling information.

17. The computer readable storage medium of claim 15, wherein determining the collaborative rating comprises retrieving one or more ratings for the candidate activity submitted by other users in a same profile group to which the user is matched.

18. The computer readable storage medium of claim 15, wherein determining the user'"'"'s preference comprises receiving preference information from the user.

19. The computer readable storage medium of claim 15, wherein determining the user'"'"'s interest comprises:

extracting a set of keywords from a set of content previously accessed by the user; and

determining whether a description of the candidate activity matches any of the keywords.

20. The computer readable storage medium of claim 15, wherein determining the proximity comprises computing a distance between a location associated with the candidate activity and a location associated with the user.

21. The computer readable storage medium of claim 20, wherein determining the proximity further comprises:

receiving a set of GPS coordinates; and

computing a motion range for the user.