METHOD FOR PERSONALIZING INFORMATION RETRIEVAL IN A COMMUNICATION NETWORK

• US 20110055186A1
• Filed: 07/05/2010
• Published: 03/03/2011
• Est. Priority Date: 09/02/2009
• Status: Active Grant

• Current Assignee: Xurmo Technologies Private Limited
• Original Assignee: Xurmo Technologies Private Limited
• Inventors: Gopalakrishnan, Sridhar
• Granted Patent: US 8,386,482 B2
• Time in Patent Office: Days
• US Class Current: 707/706
• 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

The various embodiments herein provide a method and system for personalizing information retrieval in a communication network. The method comprising receiving one or more inputs from a plurality of sources, processing information present in the one or more inputs received from the plurality of sources, performing a contextual and personalized search based on the inputs received and displaying one or more results in support of the information in the communication network. The system herein comprising an application server for receiving one or more inputs from a plurality of sources, a processor for performing at least one of processing an information present in the inputs received from the plurality of sources, generating one or more virtual documents based on the processing of the information present in the one or more inputs, performing a search based on the virtual documents generated and a display unit for displaying one or more results.

Claims

1. A method for personalizing information retrieval in a communication network, the method comprising:

receiving one or more inputs from a plurality of sources;

processing information present in the one or more inputs received from the plurality of sources;

performing a contextual and personalized search based on the inputs received; and

displaying one or more results in support of the information in the communication network.

2. The method according to claim 1, wherein the plurality of sources comprising:

an instant messenger;

an electronic mail;

a short message service;

a browser;

a customized client application;

a voice command; and

an incumbent information source.

3. The method according to claim 1, wherein the one or more inputs comprising at least one of:

a user interaction; and

a prevalent information content.

4. The method according to claim 1, wherein receiving one or more inputs from a plurality of sources comprising:

establishing a customized connection to the communication network; and

extracting information present in the plurality of sources.

5. The method according to claim 4, wherein establishing a customized connection to the communication network comprising at least one of:

establishing or connecting to an authenticated network and enabling privileged access to information in the communication network; and

developing suitable interfaces for efficient extraction and search of information in the network.

6. The method according to claim 1, wherein processing the information comprising:

refining, enhancing and synthesizing information present in the one or more inputs.

7. The method according to claim 6, wherein refining the information further comprising:

removing noise or unwanted information present in the one or more inputs of the plurality of sources through the use of filters such as a stop word filter and a histogram filter.

8. The method according to claim 6, wherein the enhancing the information comprising:

determining associations between words; and

determining strength between associations of words.

9. The method according to claim 8, wherein determining association between words and the strength between associations of words is based on developing an aweb of words.

10. The method according to claim 9, wherein the web of words called Word-Web is built using statistical, mathematical and semantic data models.

11. The method according to claim 10, wherein developing the Word-Web uses at least one of:

a word net;

one or more public websites;

one or more domain specific websites;

one or more predefined documents; and

user inputs.

12. The method according to claim 10, the Word-Web is an ever growing corpus of words across various languages connected to each other in a non-hierarchical pattern.

13. The method according to claim 10, wherein the Word-Web circumvents the need for W3C prescribed semantic models.

14. The method according to claim 10, wherein the associations of words are re-molded continuously to reflect the search engines increasing understanding of the environment.

15. The method according to claim 10, wherein the Word-Web is deployed over heterogeneous networks such as enterprise network, public networks and consumer groups.

16. The method according to claim 15, wherein a unified Word-Web which is an amalgamation of the Word-Web deployed over different networks developed to form a semantic dictionary adapted to understand words across languages and create meaningful associations between them.

17. The method according to claim 10, wherein a user-specific version of the Word-Web is developed capturing each user'"'"'s perspective relationship between words.

18. The method according to claim 10, wherein the intelligent Word-Web is versatile and created for various levels of granularity to capture nuances at the user level and the network level.

19. The method according to claim 6, wherein synthesizing the information further comprising:

building a social graph which is a web that defines the nature of interconnections between people in the network.

20. The method according to claim 6, wherein synthesizing the information further comprising:

creating profiles of people and artifacts in the communication network.

21. The method according to claim 20, wherein profiles of people and artifacts in the network are represented as virtual profile documents.

22. The method according to claim 20, wherein each user'"'"'s profile consists of a user specific version of the Word-Web, the relative strength of the user'"'"'s association with the words and the user'"'"'s relationship with information assets including people and artifacts in the network.

23. The method according to claim 20, wherein artifact profiles includes the content in the artifacts, its metadata and the people associated with in the communication network.

24. The method according to claim 20, wherein the personalized information retrieval search engine algorithms constantly evaluate effectiveness of relationships between words, people and documents so that the accuracy of the user profiles do not diminish with time.

25. The method according to claim 6, wherein synthesizing the information further comprising:

generating a virtual query document.

26. The method according to claim 25, wherein a query document comprises at least one of the words relevant to the query post processing.

27. The method according to claim 26, wherein the processing of query involves refining, enhancing and boosting the query by performing natural language processing, by using noise filters such as stop word filters, histogram filters, enhancers such as co-locaters, stemmers and lemmatizers and by using the Word-Web.

28. The method according to claim 1, wherein performing the search comprising:

performing an iterative comparison between the information present in query documents with the information of the profile documents; and

personalizing one or more results that are to be displayed based on the profile document of a user and the social graph of the network.

29. The method according to claim 1, further comprising:

displaying the results based on the inputs from the plurality of sources; and

refining the one or more results displayed.

30. The method according to claim 29, wherein displaying the results comprising showcasing details of the relevant people, artifacts, archives, advertisements and derived information in the network.

31. The method according to claim 29, wherein displaying the results comprising providing an analysis on the data in the communication network based on the criteria defined by the user

32. The method according to claim 29, wherein the result refining due to user or system inputs comprising:

modifying the information present in the query documents;

updating user profiles based on the feedback from refining results; and

updating the intelligent Word-Web.

33. The method according to claim 29, wherein the result refining further comprising:

performing a search based on the updated query and profile documents; and

displaying a personalized and context relevant one or more results.

34. A system for personalizing information retrieval in a communication network, the system comprising:

an application server adapted to;

receive one or more inputs from a plurality of sources;

a processor adapted to;

process an information present in the one or more inputs received from the plurality of sources;

generate one or more virtual documents based on the processing of the information present in the one or more inputs;

perform a search based on the virtual documents generated; and

a display unit for displaying one or more results based on the document generated for the information.

35. A system according to claim 34, wherein the processor comprises:

a query refiner for refining the information present in the one or more inputs;

a query enhancer for enhancing the information present in the one or more inputs of the plurality of sources;

a query booster for boosting the information present in the one or more inputs of the plurality of sources; and

a query document generator for generating a virtual document based on the processing of the information.

36. A system according to claim 34, wherein the processor further comprises:

a Word-Web generator to generate an intelligent web of words from the one or more inputs of the plurality of sources;

a content indexer and profiler to extract information from the one or more inputs of the plurality of sources and build people and artifact profiles;

a content refiner for refining the contents from the one or more inputs of the plurality of sources;

a content modeler and profile document generator to assign appropriate weights and generate virtual profile documents for people and artifacts;

a search engine for performing an iterative comparison between the information present in a query document with the information of the profile document;

a personalize module for personalizing one or more results that to be displayed based on comparison of the results with the profile document of a user; and

a search result refiner to refine search results based on user inputs.