System and method for adaptive text recommendation

• US 20090089272A1
• Filed: 12/03/2004
• Published: 04/02/2009
• Est. Priority Date: 11/27/2000
• Status: Active Grant

• Current Assignee: Quest Software, Inc.
• Original Assignee: SonicWALL, Inc. (SonicWall Holdings Ltd.)
• Inventors: Oliver, Jonathan James, Buntine, Wray Lindsay, Roumeliotis, George
• Granted Patent: US 8,645,389 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: H04L 51/18 Commands or executable codes
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

Network system provides a real-time adaptive recommendation set of documents with a high statistical measure of relevancy to the requestor device. The recommendation set is optimized based on analyzing the text of documents of the interest set, categorizing these documents into clusters, extracting keywords representing the themes or concepts of documents in the clusters, and filtering a population of eligible documents accessible to the system utilizing site and or Internet-wide search engines. The system is either automatically or manually invoked and it develops and presents the recommendation set in real-time; for example, upon logging onto a web site or as the client views additional documents or pages of a website. The recommendation set may be presented as a greeting, notification, alert, HTML fragment, fax, voicemail, or automatic classification or routing of customer e-mail, personal e-mail, job postings, and offers for sale or exchange.

Claims

1. A method for adaptive information recommendation, the method comprising:

clustering an interest set of documents associated with a user into one or more clusters;

extracting a keyword for the one or more clusters that represents the theme of the documents in the one or more clusters;

filtering an eligible set of documents to meet an application criterion; and

adaptively constructing a recommended set of documents for each cluster of the one or more clusters.

26. The method of claim 1, wherein clustering an interest set of documents comprises:

assembling the interest set of documents;

pre-processing words of the interest set of documents; and

grouping documents from the interest set of documents into the clusters utilizing a clustering algorithm that maximizes the cluster score of the clusters.

27. The method of claim 1, wherein the interest set of documents comprises a previously accessed document.

28. The method of claim 1, wherein clustering an interest set of documents into one or more clusters comprises:

removing common words in the language used in the application;

removing words that are not significant for the application; and

grouping documents from the interest set of documents into the one or more clusters.

29. The method of claim 1, wherein the extracting a keyword for the one or more clusters includes calculating a plurality of keyword scores corresponding to the one or more clusters and selecting the keyword that maximizes the keyword score of the cluster.

31. The method of claim 1, wherein constructing the recommended set of documents comprises:

calculating a relevance score of each document in the eligible set of documents;

selecting documents of the eligible set of documents with high relevance scores; and

applying a selection criterion measuring popularity of the document in the eligible set of documents and/or client preference for the document in the eligible set of documents.

36. The method of claim 1, Wherein the interest set of documents is an interest set of offer descriptions, the keyword represents the theme of the offer descriptions, the eligible set of documents is an eligible set of offer descriptions, and the recommended set of documents is a recommended set of offer descriptions.

2-25. -25. (canceled)

30. (canceled)

32-35. -35. (canceled)

37. A computer-readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for adaptive information recommendation, the method comprising:

clustering an interest set of documents associated with a user into one or more clusters;

extracting a keyword for the one or more clusters that represents the theme of the documents in the one or more clusters;

filtering an eligible set of documents to meet an application criterion; and

adaptively constructing a recommended set of documents for each cluster of the one or more clusters.

39. The computer-readable storage medium of claim 37, wherein clustering an interest set of documents comprises:

assembling the interest set of documents;

pre-processing words of the interest set of documents; and

grouping documents from the interest set of documents into the clusters utilizing a clustering algorithm that maximizes the duster score of the clusters.

40. The computer-readable storage medium of claim 37, wherein the interest set of documents comprises a previously accessed document.

41. The computer-readable storage medium of claim 37, wherein clustering an interest set of documents into one or more clusters comprises:

removing common words in the language used in the application;

removing words that are not significant for the application; and

grouping documents from the interest set of documents into the one or more clusters.

42. The computer-readable storage medium of claim 37, wherein the extracting a keyword for the one or more clusters includes calculating a plurality of keyword scores corresponding to the one or more clusters and selecting the keyword that maximizes the keyword score of the cluster.

44. The computer-readable storage medium of claim 37, wherein constructing the recommended set of documents comprises:

calculating a relevance score of each document in the eligible set of documents;

selecting documents of the eligible set of documents with high relevance scores; and

applying a selection criterion measuring popularity of the document in the eligible set of documents and/or client preference for the document in the eligible set of documents.

49. The computer-readable storage medium of claim 37, wherein the interest set of documents is an interest set of offer descriptions, the keyword represents the theme of the offer description, the eligible set of documents is an eligible set of offer descriptions, and the recommended set of documents is a recommended set of offer descriptions.

38. (canceled)

43. (canceled)

45-48. -48. (canceled)

50. An apparatus for providing adaptive information Recommendations, comprising:

a processor configured to execute instructions for;

clustering an interest set of documents associated with a user into one or more clusters,

extracting a keyword for the one or more clusters that represents the theme of the documents in the one or more clusters,

filtering an eligible set of documents to meet an application criterion, and

adaptively constructing a recommended set of documents for each cluster of the one or more clusters; and

a memory coupled to the processor, the memory configured to store the instructions.