SYSTEM AND METHODS FOR PROVIDING CONTENT VIA THE INTERNET

• US 20130159254A1
• Filed: 12/14/2011
• Published: 06/20/2013
• Est. Priority Date: 12/14/2011
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Xu, Zhichen, Chen, Wen-Yen
• Application Number:
  US13/325,121
• Publication Number:
  US 20130159254A1
• Time in Patent Office: Days
• US Class Current: 707/639
• 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

Systems and methods to enhance enhancing a service for a user. The system collecting documents viewed or words posted by a user. Determining a list of topic words for the user based on words in the documents viewed or words posted. Identifying a list of topic words associated with the user, based on words in the one or more documents and the words posted by the user. Assigning each of the topic words to at least one of a plurality of topics based on correlations between the topic words of the user and topic words from other users. Estimating a set of interest topics for the user based on the topics assigned to the topic words of the user.

Claims

1. A method implemented on a machine having at least one processor, storage, and a communication platform connected to a network for enhancing a service for a user comprising:

collecting one or more documents viewed by the user or words posted by the user;

identifying a list of topic words associated with the user based on words in the one or more documents and the words posted by the user;

assigning each of the topic words to at least one of a plurality of topics based on correlations between the topic words of the user and topic words from other users; and

estimating a set of interest topics for the user based on the topics assigned to the topic words of the user.

2. The method of claim 1, further comprising filtering the one or more documents for the user to remove at least one of images, graphics, scripting language, or formatting characters from the one or more documents before determining a list of topic words for the user.

3. The method of claim 1, further comprising:

building a lexicon of unique words contained in the list of topic words for the user and the other users; and

calculating a likelihood that each word in the lexicon is associated with each of the plurality of topics based on a number of times each of the topic words is assigned to a one of the plurality of topics.

4. The method of claim 1, further comprising providing the service to the user based on the set of interest topics associated with the user.

5. The method of claim 1, further comprising customizing content provided to the user based on the set of interest topics associated with the user.

6. The method of claim 3, further comprising merging the estimated set of interest topics for the user and the likelihood that each word in the lexicon is associated with each of the plurality of topics with declared interest data for the user from a first database in a second database.

7. The method of claim 6, further comprising at least one of making a snapshot of the second database or customizing web pages for the user based on the data regarding the user in second database.

8. A method, implemented on a machine having at least one processor, storage, and a communication platform connected to a network for enhancing a service for a user, comprising the steps of:

receiving, via the communication platform, documents viewed or words posted by the user;

determining a list of topic words for the user based on words in the documents and words posted by the user;

calculating a likelihood that each user is interested in one of a plurality of topics based on correlations between the topic words in the list of the topic words for the user and topic words in lists of topic words for other users;

determining a list of unique words from the list of the topic words for the user and the topic words in lists of topic words for the other users; and

calculating a likelihood that each unique word belongs to each of the number of topics based on correlations between the topic words for the user and the topic words in lists of topic words for the other users.

9. The method of claim 8, further comprising:

receiving, via the communication platform, a request for topics of interest of the user, and

sending information regarding interests of the user based on the likelihood that each user is interested in one of a plurality of topics.

10. A machine-readable tangible and non-transitory medium having information recorded thereon, wherein the information, when read by a machine, causes the machine to perform a method of enhancing a service for a user comprising:

collecting, via a communication platform, documents viewed or words posted by the user;

determining a list of topic words for the user based on words in the documents viewed or words posted by the user;

assigning each of the topic words to at least one of a plurality of topics based on correlations between topic words of the user and topic words of other users; and

estimating a set of interest topics for the user based on the topics assigned to the topic words of the user.

11. The machine-readable tangible and non-transitory medium of claim 10, the method further comprising providing the service to the user based on the set of interest topics associated with the user.

12. The machine-readable tangible and non-transitory medium of claim 10, the method further comprising:

receiving, via the communication platform, a request for topics of interest of the user; and

sending information regarding interests of the user based on the estimated set of interest topics for the user.

13. A system for enhancing a service for a user comprising:

a server that delivers the service to the user;

a first database coupled to the server that stores preferences or declared interests of the user;

a second database coupled to the server that stores information regarding documents viewed by or words posted by the user;

a topic mining engine that estimates an interest topic of the user based on correlations between a use by the user of topic words in the documents viewed by or posted the user and a use of the topic words by other users; and

a third database that stores the interest topic of the user estimated by the topic mining engine, wherein the server delivers the service to the user based on data in the third database associated with the user.

14. The system of claim 13, further comprising:

a text processor coupled to the first database that extracts the topic words from the documents viewed by or posted by the user.

15. The system of claim 13, further comprising:

an updater, coupled to the topic mining engine and the user database that combines the interest topic of the user estimated by the topic mining engine with the declared interests of the users and stores the combined interests in the third database.

16. The system of claim 13, wherein the server is coupled to the third database, and the server is adapted to customize the service delivered to the users based on the combined interests stored in the third database.

17. The system of claim 13, further comprising a dumper coupled to the third database that is adapted to create a snapshot of the third database and adapted to store the snapshot in a fourth database.

18. The system of claim 13, wherein the topic mining engine is adapted to build a lexicon of unique words used in the documents viewed by or words posted by the user.

19. The system of claim 18, wherein

the topic mining engine is further adapted to calculate a likelihood that each unique word in the lexicon is associated with each of the number of topics;

and

the third database further stores the likelihood that each unique word in the lexicon is associated with each of the number of topics.

20. The system of claim 19, wherein the topic mining engine is adapted to assign each topic word to one of a plurality of topics including the interest topic, and calculate the likelihood of the interest topic of the user based on a number of the topic words for the user assigned to the interest topic and a likelihood that each unique word in the lexicon is associated with the interest topic based on a number of times each topic word is assigned to the interest topic.