Method and apparatus for tailoring content of information delivered over the internet

• US 7,735,013 B2
• Filed: 03/16/2001
• Issued: 06/08/2010
• Est. Priority Date: 03/16/2001
• Status: Expired due to Fees

• Current Assignee: eBay Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Shaouy, William Philip, Trevathan, Matthew Bunkley
• Primary Examiner(s): KE, PENG
• Application Number:
  US09/810,992
• Publication Number:
  US 20020130902A1
• Time in Patent Office: 3,371 Days
• Field of Search: 345/745, 345/746, 345/747, 345/749, 345/800, 345/731, 345/720, 345/854, 345/744, 715/745, 715/749, 715/744, 715/800, 715/731, 715/720, 715/854, 715/746, 715/747
• US Class Current: 715/745
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9536 Search customisation based on social or collaborative filtering

Abstract

Adapting information to a user of an application program is provided. An arbiter receives a request object from the application program. The request object contains profile elements that convey characteristics of the user. The profile elements are analyzed by the arbiter, and, based on the outcome of the analysis, the arbiter selects a personalization engine from a plurality of personalization engines. The request object is passed to the selected personalization engine, which accesses a content database to retrieve a personalized content object comprising information tailored to the user. The personalized content object is sent to the application program, which interprets it for the user. Various embodiments include an expert-system arbiter, and an arbiter comprising computer code that is provided according to conventional object-oriented analysis and design methods executing on a programmable processor. The plurality of personalization engines may include a rule-based engine, a collaborative-filtering engine, or a predictive-modeling engine.

Claims

1. A method executable by a processor for tailoring information to characteristics of an information user, comprising:

passing a request object excluding any profile elements to an input logic using the processor;

receiving the request object and accessing a profile database through a profile database proxy using the processor, the profile database containing profile elements that are known to a server but originally excluded from the request object, the profile elements including a user name, network ID, and user interaction history;

incorporating the request object with relevant profile elements of the profile elements found in the profile database using the processor;

passing the request object with the relevant profile elements to an arbiter using the processor;

actively selecting, by analysis of the relevant profile elements using the processor, a personalization engine, which is configured to provide an optimal performance, from a plurality of personalization engines by the arbiter, the arbiter refining and altering a selection based on a number and type of the relevant profile elements, wherein the plurality of personalization engines are a collaborative filtering engine, a predictive-modeling personalization engine, and a business-rules engine, the collaborative filtering engine provides an optimal performance when information is known about a group of users based on statistical knowledge, the predictive-modeling personalization engine provides an optimal performance when a user is unknown based on a short-term usage path of the user, and the business-rules engine provides an optimal performance when the personalization engine needs to change in response to one or more changing circumstances;

accessing a content database via a content database proxy to retrieve a personalized content object identified by the personalization engine selected by the arbiter using the processor; and

passing with the arbiter the personalized content object to an application program,

wherein the arbiter comprises an expert system that is one of rule based, model based, and knowledge based.

2. The method of claim 1, further comprising using the arbiter for on-line shopping.

3. The method of claim 1, wherein the application program is a web browser.

4. The method of claim 1, further comprising sending the request object over a communication network.

5. The method of claim 4, wherein the communication network is the Internet.

6. The method of claim 1, wherein the plurality of personalization engines comprises at least two personalization engines selected from the group consisting of a rule-based personalization engine, a predictive-modeling personalization engine, and a collaborative filtering personalization engine.

7. The method of claim 1, further comprising the arbiter analyzing at least one of a date of the request object, a user identity, a user shopping history, and a user usage path.

8. The method of claim 1, further comprising using the arbiter for on-line shopping, wherein the application program is a web browser, wherein the request object is an HTTP message and contains data regarding characteristics of a user.

9. The method of claim 8, wherein the request object is sent from the application program to a server.

10. Apparatus for tailoring information in a combination of hardware and software to characteristics of an information user, the apparatus comprising:

a content database;

an input logic for receiving a request object excluding any profile elements and accessing a profile database through a profile database proxy, the profile database containing profile elements that are known to a server but originally excluded from the request object, the input logic configured to incorporate into the request object any relevant profile elements of the profile elements found in the profile database including a user name, network ID, and user interaction history;

an arbiter for accepting and analyzing a request object having the relevant profile elements, which is passed by the input logic, the arbiter refining and altering a selection based on a number and type of at least one of the profile elements contained in the request object;

a plurality of personalization engines for selecting at least one personalized content object from the content database, wherein the plurality of personalization engines are a collaborative filtering engine, a predictive-modeling personalization engine, and a business-rules engine, the collaborative filtering engine provides an optimal performance when information is known about a group of users based on statistical knowledge, the predictive-modeling personalization engine provides an optimal performance when a user is unknown based on a short-term usage path of the user, and the business-rules engine provides an optimal performance when the personalization engine needs to change in response to one or more changing circumstances;

the arbiter selecting a personalization engine from the plurality of personalization engines, and the selected personalization engine selects the at least one personalization content object from the content database via a content database proxy; and

the arbiter passing the personalized content object to an application program,

wherein the arbiter comprises an expert system that is one of rule based, model based, and knowledge based.

11. The apparatus of claim 10, further comprising output logic for passing the at least one personalization content object to an application program over a communication network.

12. The apparatus of claim 11, wherein the communication network is the Internet.

13. The apparatus of claim 11, wherein the application program is a web browser.

14. The apparatus of claim 10, wherein the arbiter is configured to receive the request object from a user and the profile elements from the profile database.

15. The apparatus of claim 10, wherein the arbiter is configured to analyze at least one of a date of the request object, a user identity, a user shopping history, and a user usage path.

16. The apparatus of claim 10 wherein the arbiter is utilized for on-line shopping, wherein the application program is a web browser, wherein the request object is an HTTP message and contains data regarding characteristics of a user.

17. The apparatus of claim 16, wherein the request object is sent from the application program to the server.

18. A method executable by a processor for tailoring information delivered to a user, comprising:

passing a request object excluding any profile elements to an input logic using the processor;

receiving the request object and accessing a profile database through a profile database proxy using the processor, the profile database containing profile elements that are known to a server but originally excluded from the request object, the profile elements including a user name, network ID, and user interaction history;

incorporating the request object with relevant profile elements of the profile elements found in the profile database using the processor;

passing the request object with the relevant profile elements to an arbiter using the processor;

selecting with the arbiter a personalization engine by analysis of the relevant profile elements, wherein the personalization engine is at least one of a collaborative filtering engine, a predictive-modeling personalization engine, and a business-rules engine, the collaborative filtering engine provides an optimal performance when information is known about a group of users based on statistical knowledge, the predictive-modeling personalization engine provides an optimal performance when a user is unknown based on a short-term usage path of the user, and the business-rules engine provides an optimal performance when the personalization engine needs to change in response to one or more changing circumstances;

selecting with the personalization engine a personalized content object to tailor information provided to the user, wherein the personalized content object is stored in a content database and accessed via a content database proxy; and

using the arbiter for on-line shopping,

wherein the arbiter comprises an expert system that is one of rule based, model based, and knowledge based.

19. The method of claim 18, further comprising the arbiter receiving the request object from a user, and sending the selected personalized content object to the user'"'"'s application program.

20. The method of claim 19, wherein the application program is a web browser.

21. The method of claim 18, further comprising the arbiter receiving the profile elements from the profile database.

22. The method of claim 18, further comprising sending the request object over a communication network.

23. The method of claim 22, wherein the communication network is the Internet.