RECOMMENDATION SYSTEM WITH MULTIPLE INTEGRATED RECOMMENDERS

• US 20090006373A1
• Filed: 06/29/2007
• Published: 01/01/2009
• Est. Priority Date: 06/29/2007
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Kim, Sung H., Lam, Shing Yan, Ionkov, George M., Chan, James D., Witt, Brett W., Chakrabarti, Kushal
• Granted Patent: US 7,949,659 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536

Abstract

A recommendations system is provided in various embodiments for selecting items to recommend to a user. The system includes a recommendation engine with a plurality of recommenders, and each recommender identifies a different type of reason for recommending items. In one embodiment, each recommender retrieves item preference data and generates candidate recommendations responsive to a subset of that data. The recommenders also score the candidate recommendations. In certain embodiments, a normalization engine normalizes the scores of the candidate recommendations provided by each recommender. A candidate selector selects at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user. The candidate selector also outputs the recommendations with associated reasons for recommending the items.

Claims

1. A recommendations system for selecting items to recommend to a user, the system comprising:

a recommendation engine comprising a plurality of recommenders, each recommender corresponding to a different type of reason for recommending items, each recommender configured to;

retrieve item preference data reflective of actions performed by a user;

generate candidate recommendations responsive to a subset of the item preference data,

identify one or more reasons for recommending the candidate recommendations, and

score the candidate recommendations to provide relative indications of the strength of the candidate recommendations;

a normalization engine configured to normalize the scores of the candidate recommendations provided by each recommender; and

a candidate selector component configured to;

select at least a portion of the candidate recommendations based on the normalized scores to provide as recommendations to the user, and

output the recommendations with associated reasons for recommending the items.

2. The system of claim 1, wherein at least some of the recommenders are modular, such that the recommenders can be selectively added to or removed from the recommendation engine.

3. The method of claim 1, wherein the different type of reason for recommending items is that a recommendation could complete a series of items owned by the user.

4. The method of claim 1, wherein the different type of reason for recommending items is that a recommendation could be used as a gift for a friend'"'"'s upcoming birthday.

5. A computer-implemented method of selecting items to recommend, the method comprising:

retrieving item preference data reflective of actions performed by a user;

providing the item preference data to a plurality of recommenders, each recommender corresponding to a different type of reason for recommending items, each recommender configured to;

generate candidate recommendations responsive to a subset of the item preference data, and

identify one or more reasons for recommending the candidate recommendations;

selecting at least a portion of the candidate recommendations to provide as recommendations to the user; and

outputting the recommendations with associated reasons for recommending the items.

6. The method of claim 5, wherein each recommender is further configured to generate scores for the candidate recommendations.

7. The method of claim 6, further comprising normalizing the scores from each recommender.

8. The method of claim 5, wherein at least some of the recommenders are modular, such that the recommenders can be selectively added to or removed from a recommendation engine.

9. The method of claim 5, wherein the different type of reason for recommending items is that a recommendation could complete a series of items owned by the user.

10. The method of claim 5, wherein the different type of reason for recommending items is that a recommendation could be used as a gift for a friend'"'"'s upcoming birthday.

11. The method of claim 5, wherein the different type of reason for recommending items is that a recommendation is similar to an item searched for by the user.

12. The method of claim 5, wherein the different type of reason for recommending items is that a recommendation is an item that might be purchased by an early adopter, and the user is an early adopter.

13. The method of claim 5, wherein the different type of reason for recommending items is that a recommendation is a top seller in a browse node of interest to the user.

14. The method of claim 5, wherein the item preference data comprises data selected from the group consisting of browse history data, series data, search data, purchase history data, friends data, browsing interests data, tags data, and top sellers data.

15. The method of claim 5, wherein each recommender is further configured to provide one or more reason types that map to reason text.

16. A computer-readable medium having instructions stored thereon which cause a general-purpose computer to perform a method of selecting items to recommend to a user, the method comprising:

retrieving item preference data reflective of actions performed by a user;

providing the item preference data to a plurality of recommenders, each recommender corresponding to a different type of reason for recommending items, each recommender configured to;

generate candidate recommendations responsive to a subset of the item preference data, and

identify one or more reasons for recommending the candidate recommendations; and

selecting at least a portion of the candidate recommendations to provide as recommendations to the user.

17. The method of claim 16, further comprising outputting the recommendations with associated reasons for recommending the items.

18. An apparatus for selecting items to recommend to a user, the apparatus comprising:

means for retrieving item preference data reflective of actions performed by a user;

means for providing the item preference data to a plurality of recommenders, each recommender corresponding to a different type of reason for recommending items, each recommender configured to;

generate candidate recommendations responsive to a subset of the item preference data, and

identify one or more reasons for recommending the candidate recommendations; and

means for selecting at least a portion of the candidate recommendations to provide as recommendations to the user.

19. The apparatus of claim 18, further comprising means for outputting the recommendations with associated reasons for recommending the items.