OPTIMIZATION OF A WEB-BASED RECOMMENDATION SYSTEM

• US 20120143718A1
• Filed: 12/03/2010
• Published: 06/07/2012
• Est. Priority Date: 12/03/2010
• Status: Abandoned Application

• Current Assignee: Choicestream Incorporated
• Original Assignee: Choicestream Incorporated
• Inventors: Graham, Stephen E., Graham, Mary Catherine, Ben-Akiva, Moshe, Roberts, Adam M., Li, Xiaojing, Rathi, Vaibhav, Mohanty, Sanjib
• Application Number:
  US12/959,745
• Publication Number:
  US 20120143718A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A method for determining product recommendations to be presented to users includes forming, by a formula generation module, a plurality of different recommendation formulas, including, for each recommendation formula, assigning a weight to at least some of a plurality of recommendation characteristics, wherein each recommendation characteristic is representative of at least one of a characteristic of a product, a characteristic of a method for presenting the product recommendations to the users, and a characteristic of a user. The method further includes iteratively performing the steps of: for each of the plurality of recommendation formulas, selecting, by a product recommendation module, at least one product for presentation to the users on the basis of the corresponding recommendation formula; sending, by a communications module, instructions to a server to present the selected product to the users; receiving, by a data evaluation module, data representative of user responses to each of the products presented to the users; evaluating, by the data evaluation module, the received data; and selecting, using the data evaluation module, a subset of the recommendation formulas included in the plurality of recommendation formulas on the basis of the evaluation of the collected data.

Claims

1. A method for determining product recommendations to be presented to users, the method comprising:

forming, by a formula generation module, a plurality of different recommendation formulas, including, for each recommendation formula, assigning a weight to at least some of a plurality of recommendation characteristics, wherein each recommendation characteristic is representative of at least one of a characteristic of a product, a characteristic of a method for presenting the product recommendations to the users, and a characteristic of a user;

iteratively performing the steps of;

for each of the plurality of recommendation formulas, selecting, by a product recommendation module, at least one product for presentation to the users on the basis of the corresponding recommendation formula;

sending, by a communications module, instructions to a server to present the selected product to the users;

receiving, by a data evaluation module, data representative of user responses to each of the products presented to the users;

evaluating, by the data evaluation module, the received data; and

selecting, using the data evaluation module, a subset of the recommendation formulas included in the plurality of recommendation formulas on the basis of the evaluation of the collected data.

2. The method of claim 1, wherein the characteristic of the method for presenting the product recommendations to the user includes at least one of a degree of variety in the presented product recommendations, a degree of randomization of the presented product recommendations, and a degree of filtering of the presented product recommendations.

3. The method of claim 1, wherein the characteristic of a user includes at least one of a purchasing history of the user, a browsing history of the user, and a demographic characteristic of the user.

4. The method of claim 3, wherein forming the plurality of recommendation formulas includes determining a length in time of at least one of the purchasing history of the user and the browsing history of the user.

5. The method of claim 1, wherein selecting at least one product includes selecting at least one product further on the basis of a characteristic of the product.

6. The method of claim 1, wherein receiving data representative of user responses includes receiving data representative of a performance metric.

7. The method of claim 6, wherein the performance metric includes at least one of a click through rate, a click conversion rate, a click purchase rate, a click revenue, a view through conversion rate, a view through purchase rate, a click average order size, a view through average order size, a view through revenue, and a total revenue.

8. The method of claim 1, wherein evaluating the received data includes evaluating the data on the basis of a performance metric.

9. The method of claim 8, wherein the performance metric includes at least one of a click through rate, a click conversion rate, a click purchase rate, a click revenue, a view through conversion rate, a view through purchase rate, a click average order size, a view through average order size, a view through revenue, and a total revenue.

10. The method of claim 8, wherein evaluating the received data includes identifying at least one recommendation formula for which a value associated with the performance metric of the collected data corresponding to the at least one identified recommendation formula exceeds a predetermined threshold value.

11. The method of claim 10, wherein selecting the subset of the recommendation formulas including eliminating the at least one recommendation formula for which the value associated with the performance metric of the collected data corresponding to the selected at least one recommendation formula is less than the predetermined threshold value.

12. The method of claim 10, wherein the value associated with the performance metric is a confidence level representative of a relative standing of the performance metric.

13. The method of claim 8, wherein evaluating the received data includes identifying at least one recommendation formula for which the performance metric of the collected data corresponding to the identified at least one recommendation formula is below a predetermined threshold value.

14. The method of claim 1, wherein evaluating the received data includes:

fitting a surface to the collected data; and

smoothing the surface.

15. The method of claim 14, wherein the surface is representative of a value of a performance metric associated with each of the plurality of recommendation formulas.

16. The method of claim 1, further comprising, for each of the subset of the recommendation formulas, selecting, by the product recommendation module, at least one product for presentation to the users on the basis of the corresponding recommendation formula.

17. The method of claim 1, further comprising accepting, at the formula generation module, the plurality of recommendation characteristics.

18. A system for determining product recommendations to be presented to users, the system comprising:

a formula generation module configured to form a plurality of different recommendation formulas, including, for each recommendation formula, assigning a weight to at least some of a plurality of recommendation characteristics, wherein each recommendation characteristic is representative of at least one of a characteristic of a product, a characteristic of a method for presenting the product recommendations to the users, and a characteristic of a user;

a product recommendation module configured to select, for each of the plurality of recommendation formulas, at least one product for presentation to the users on the basis of the corresponding recommendation formula;

a communications module configured to send instructions to a server to present the selected product to the users; and

a data evaluation module configured to perform the steps of;

receiving data representative of user responses to each of the products presented to the users;

evaluating the received data; and

selecting a subset of the recommendation formulas included in the plurality of recommendation formulas on the basis of the evaluation of the collected data.