Adaptive multi-channel content selection with behavior-aware query analysis

• US 20090265325A1
• Filed: 04/16/2008
• Published: 10/22/2009
• Est. Priority Date: 04/16/2008
• Status: Active Grant

• Current Assignee: IAC Search & Media Incorporated (Match Group, Inc.)
• Original Assignee: IAC Search & Media Incorporated (Match Group, Inc.)
• Inventors: Ding, Lei, Miao, Jingheo, Bhatnagar, Neeraj, Agrawal, Divyakant, Chrabakh, Wahid, Yang, Tao, Camoglu, Orhan
• Granted Patent: US 8,751,481 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Systems and methods for selecting content from a plurality of channels in response to a received query are described. The systems and methods optimize content selection based on received queries and an identification of the user together with analysis of historical data.

Claims

1. A method comprising:

receiving a search query from a user;

identifying web search results corresponding to the search query;

optimizing content selection from a plurality of channels based on the query and the user; and

presenting the web search results and selected content from the plurality of channels to the user.

2. The method of claim 1, wherein optimizing content selection from a plurality of channels based on the query and the user comprise optimizing content selection from a plurality of channels based on the query, the user, and a time range.

3. The method of claim 2, wherein the time range comprises an event.

4. The method of claim 1, wherein optimizing content selection from a plurality of channels comprises analyzing historical data based on the query and the user.

5. The method of claim 4, wherein the historical data comprises past click results of the user.

6. The method of claim 4, wherein the historical data comprises past click results associated with the query.

7. The method of claim 1, further comprising:

identifying query concepts from the received query;

analyzing historical data based on each of the query concepts to determine a recommended channel selection; and

aggregating the recommended channel selection for each of the query concepts.

8. The method of claim 1, wherein optimizing content selection from a plurality of channels based on the query and an identification of the user comprises dynamically determining a recommended channel selection.

9. The method of claim 8, further comprising determining a recommended amount of information from each recommended channel.

10. The method of claim 1, wherein optimizing content selection from a plurality of channels comprises receiving at least one channel of the plurality of channels.

11. The method of claim 1, wherein the plurality of channels are selected from the group consisting of new images, images, video, blogs, dictionary, advertisements, sponsored results and combinations thereof.

12. The method of claim 1, wherein optimizing content selection from a plurality of channels comprises accessing information from a database associated with at least one channel of the plurality of channels.

13. A system comprising:

a memory for storing a set of instructions;

a processor connected to the memory;

a web search engine module stored in the memory and executable by the processor to receive a search query from a user, identify web search results corresponding to the search query and present the web search results to the user; and

a channel selection system module stored in the memory and executable by the processor to optimize content selection from a plurality of channels based on the query and the user.

14. The system of claim 13, further comprising a plurality of databases in the memory, the plurality of databases coupled with the web search engine module and the channel selection system module.

15. The system of claim 13, further comprising a plurality of channels coupled with the channel selection system module over a network.

16. The system of claim 14, wherein the plurality of databases, the web search engine module and the channel selection system module are located within a web search server.

17. The system of claim 14, wherein at least one of the plurality of databases comprises historical data about a plurality of users of the system and a plurality of queries received at the web search engine.

18. The system of claim 14, wherein at least one of the plurality of databases comprises vertical information.

19. A method comprising:

storing historical data including a plurality of queries and user data from a plurality of users in a database;

analyzing the stored historical data to classify a relevancy of a channel to each of the plurality of queries and user data; and

storing the classification.

20. The method of claim 19, further comprising receiving a search query from a user and analyzing the received query.

21. The method of claim 20, wherein analyzing the received query comprises:

partitioning the received query to identify concept blocks;

classifying each concept block to determine a classification decision;

aggregating the classification decision of each concept block; and

computing a channel relevancy for the query.

22. The method of claim 20, wherein analyzing the received query comprises:

deriving the probability of click for a channel based on the user and the received query; and

computing a channel relevancy for the query.

23. The method of claim 22, wherein deriving the probability of click for a channel further comprises deriving the probability of click based on an access time range.

24. The method of claim 22, further comprising determining a recommendation of channels based on the computed channel relevancy.

25. The method of claim 21, further comprising determining a recommendation of channels based on the computed channel relevancy.

26. The method of claim 19, wherein analyzing the stored historical data to determine a recommendation of a channel selection further comprises determining an amount of information from each recommended channel.