Recommendation system

• US 20060136284A1
• Filed: 12/17/2004
• Published: 06/22/2006
• Est. Priority Date: 12/17/2004
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Patt-shamir, Boaz, Peleg, David, Awerbuch, Baruch, Tuttle, Mark Rogers
• Application Number:
  US11/015,276
• Publication Number:
  US 20060136284A1
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0278 Product appraisal

Abstract

A technique for recommending products to a user (110a). According to the technique, the user requests (110a) a recommendation from a recommendation system (200). The recommendation system (200) generates a recommendation using a technique whose time complexity is less than O(m·n). The generated list is then presented to the user (110a).

Claims

1. A method for providing a recommendation of a product to a computer user, the method comprising the steps of:

generating a recommendation of a product from a set of products using a technique that is less than O(m·

n) in computational complexity with respect to time, wherein O is conventional big-O notation for on the order of, m is a number of recommenders in a set of recommenders that provide recommendations for products in the set of products and n is a number of products in the set of products; and

presenting the recommendation to the computer user.

2. A method as defined in claim 1 further comprising the steps of:

selecting a committee of recommenders from the set of recommenders;

for each recommender in the committee, probing each product in the set of products to identify a recommended product; and

generating a list of recommended products from the products recommended by each recommender in the committee.

3. A method as defined in claim 2 wherein the number of recommenders selected for the committee of recommenders is determined as κ

ln(κ

/γ

) wherein “

κ

”

is an integer that is greater than or equal to zero and represents a number of preference types, “

ln”

is the natural logarithm function and “

γ

”

is a value that is greater than zero but less than or equal to 1 and represents an error value.

4. A method as defined in claim 2 further comprising the step of:

recommending a product from the list of products.

5. A method as defined in claim 1 wherein the technique is a distributed technique.

6. A method as defined in claim 1 further comprising the steps of:

choosing a product from the set of products; and

recommending the chosen product.

7. A method as defined in claim 6 wherein the product is chosen at random.

8. A method as defined in claim 1 further comprising the steps of:

choosing a recommender from the set of recommenders; and

recommending a product that is recommended by the chosen recommender.

9. A method a defined in claim 8 wherein the recommender is chosen at random.

10. A node for providing a recommendation to a computer user, the node comprising:

a memory containing a recommendation process; and

a processor configured to execute the recommendation process, wherein the recommendation process is configured to cause the processor to;

(a) generate a recommendation of a product from a set of products using a technique that is less than O(m·

n) in computational complexity with respect to time, wherein O is conventional big-O notation for on the order of, m is a number of recommenders in a set of recommenders that provide recommendations for products in the set of products and n is a number of products in the set of products, and

(b) present the recommendation of a product to the computer user.

11. A node as defined in claim 10 wherein the recommendation process is further configured to cause the processor to:

(a) select a committee of recommenders from the set of recommenders, and

(b) generate a list of recommended products from products recommended by each recommender in the committee.

12. A node as defined in claim 11 wherein the recommendation process is further configured to cause the processor to present the generated list of recommended products to the computer user.

13. A node as defined in claim 10 wherein the recommendation process is further configured to cause the processor to:

(a) choosing a product from the set of products, and

(b) recommend the chosen product.

14. A node as defined in claim 10 wherein the recommendation process is further configured to cause the processor to:

choose a recommender from the set of recommenders, and recommend a product that is recommended by the chosen recommender.

15. A node as defined in claim 10 wherein the technique is a distributed technique.

16. An apparatus for providing a recommendation to a computer user, the apparatus comprising:

means for generating a recommendation of a product from a set of products using a technique that is less than O(m n) in computational complexity with respect to time, wherein O is conventional big-O notation for on the order of, m is a number of recommenders in a set of recommenders that provide recommendations for products in the set of products and n is a number of products in the set of products; and

means for presenting the recommendation to the computer user.

17. An apparatus as defined in claim 16 further comprising:

means for selecting a committee of recommenders from the set of recommenders; and

means for generating a list of recommended products from products recommended by each recommender in the committee.

18. An apparatus as defined in claim 16 comprising:

means for choosing a product from the set of products; and

means for recommending the chosen product.

19. An apparatus as defined in claim 16 comprising:

means for choosing a recommender from the set of recommenders; and

means for recommending a product that is recommended by the chosen recommender.

20. A computer readable medium comprising computer executable instructions for:

generating a recommendation of a product from a set of products using a technique that is less than O(m·

n) in computational complexity with respect to time, wherein O is conventional big-O notation for on the order of, m is a number of recommenders in a set of recommenders that provide recommendations for products in the set of products and n is a number of products in the set of products; and

presenting the recommendation to a computer user.