Method and Apparatus for Context-Based Content Recommendation

• US 20090037355A1
• Filed: 08/08/2008
• Published: 02/05/2009
• Est. Priority Date: 12/29/2004
• Status: Active Grant

• Current Assignee: Monetate, Inc. (StartApp, Inc.)
• Original Assignee: Baynote Incorporated (StartApp, Inc.)
• Inventors: Brave, Scott, Jia, Jack
• Granted Patent: US 8,095,523 B2
• Time in Patent Office: Days
• US Class Current: 706/45
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Starting with the people in and around enterprises, the expertise and work patterns stored in people'"'"'s brains as exhibited in their daily behavior is detected and captured. A behavioral based knowledge index is thus created that is used to produce expert-guided, personalized information.

Claims

1. A computer implemented method for content recommendation, comprising the steps of:

said computer receiving information as a result of user actions and identifying a current user'"'"'s context;

based on said identification, said computer finding an appropriate peer group that represents other users who are most like the current user in said identified context;

said computer finding content that said peer group identifies as most relevant to said current context; and

said computer generating an output comprising recommended content for display to said current user.

2. The method of claim 1, further comprising the step of:

said computer generating full-spectrum behavioral fingerprints by;

taking into account inputs comprising a wide variety of user behaviors;

cross-correlating all of these user behaviors with stored information that corresponds to the current user'"'"'s behavior'"'"'s on other pages and also with the rest of community and identified peers behaviors on a current page;

from this analysis, said computer generating a probability that the current user has found value in a particular piece of content; and

said computer storing said probability for use in a learning system.

3. A computer implemented context-centric content recommendation method, comprising the steps of:

said computer establishing a current context for a user at a Web site based upon inputs that are representative of user actions at said Web site;

said computer capturing said user'"'"'s current context information with an observer tag that is embedded in the Web site;

said computer determining said user'"'"'s interest based on various user behaviors collected by the observer tag;

said computer using full spectrum behavioral fingerprint technology to analyze said behaviors;

said computer storing all resulting information in a memory as the user'"'"'s current context vector, said context vector comprising a hybrid vector of terms and documents with weights on each entry reflecting how strongly that term or document reflects the user'"'"'s current context;

said computer incrementing said context vector entries corresponding to terms and phrases entered or clicked as a user enters search terms and/or clicks navigation links to capture expressed interest;

said computer decrementing or decaying corresponding entries as user actions move further into the past;

said computer incrementing corresponding vector entry documents that a user clicks on or indicates interest in, as determined based on a user'"'"'s implicit actions; and

from the foregoing, said computer generating a representation of the user'"'"'s current context as a context vector for use in making recommendations of content to said user.

4. The method of claim 3, further comprising the step of:

said computer expanding and refining the context vector into an intent vector, based on affinities and associations learned from aggregated wisdom collected from observations on a community as a whole over a long term;

wherein said intent vector is created by looking at affinities between terms and documents in the context vector to other terms.

5. The method of claim 4, further comprising the step of:

said computer determining affinities that allow expansion of the context vector into an intent vector with an affinity engine;

wherein said affinity engine learns connections between documents and terms, documents and documents, terms and terms, as well as users to other users, documents, and terms by watching all implicit behaviors of a user community on a Web site and by applying behavioral fingerprinting and a use rank algorithm to determine interest and associations.

6. The method of claim 5, further comprising the step of:

said computer identifying a peer group of users who share affinity to a current intent, as well as those users who exhibit behavior most like the current user within the context of that intent, said peer group being represented by a user vector, wherein each user entry in said user vector may have a weight indicating how strong of a peer he is to the current user in a current context.

7. The method of claim 6, further comprising the step of:

said computer identifying which documents have highest affinity to identified peers within a current intent;

wherein said affinity engine looks at an activeness vector, term-doc matrices, and next-step matrices associated with those peers, weighted according to their peer weight, and uses these in aggregate to computes those documents with highest affinity to the current intent; and

wherein said documents are unfiltered recommendations.

8. The method of claim 7, further comprising the step of:

said computer asking said affinity engine for community information on each of said recommendations;

wherein said information is combined with other asset information and displayed to the user to help him understand the community wisdom underlying a recommendation.

9. The method of claim 8, wherein said displayed information comprises any of a number of users in total who found value in a document, a number of peers who associated a document with a current context and/or intent, and terms and/or phrases the peer community has associated with a document.

10. The method of claim 9, wherein said terms and/or phrases comprise a virtual folksonomy that represents terms that the community has associated to a document;

wherein said virtual folksonomy is created automatically by said affinity engine based on implicit actions of the user community.

11. An apparatus for content recommendation, comprising:

means for receiving input information for use in identifying a current user'"'"'s context;

means for finding an appropriate peer group representing other users who are most like the current user in said identified context, based on said identification;

means for finding content that said peer group identifies as most relevant to said current context; and

means for generating an output comprising content recommended to said current user.

12. The apparatus of claim 11, further comprising:

means for generating full-spectrum behavioral fingerprints by;

taking into account inputs comprising a wide variety of user behaviors;

cross-correlating all of these user behaviors with the current user'"'"'s behavior'"'"'s on other pages and also with the rest of community and identified peers behaviors on a current page;

from this analysis, generating a probability that the current user has found value in a particular piece of content; and

storing said probability for use by a learning system.

13. A context-centric content recommendation apparatus, comprising:

means for receiving inputs representative of user actions and for establishing a current user context at a Web site therefrom;

an observer tag embedded in the Web site for capturing said user'"'"'s current context information;

means for determining said user'"'"'s interest based on various behaviors collected by the observer tag;

means for using full spectrum behavioral fingerprint technology to analyze said behaviors;

means for storing all resulting information in a memory as the user'"'"'s current context vector, said context vector comprising a hybrid vector of terms and documents with weights on each entry reflecting how strongly that term or document reflects the user'"'"'s current context;

means for incrementing said context vector entries corresponding to terms and phrases entered or clicked are incremented as a user enters search terms and/or clicks navigation links to capture expressed interest;

means for decrementing or decaying corresponding entries as user actions move further into the past;

means for incrementing corresponding vector entry documents that a user clicks on or indicates interest in, as determined based on a user'"'"'s implicit actions; and

means for generating a representation of the user'"'"'s current context as a context vector from the foregoing for use in making recommendations to the user.

14. The apparatus of claim 13, further comprising:

means for expanding and refining the context vector into an intent vector, based on affinities and associations learned from aggregated wisdom collected from observations on a community as a whole over a long term;

wherein said intent vector is created by looking at affinities between terms and documents in the context vector to other terms.

15. The apparatus of claim 14, further comprising:

means for determining affinities that allow expansion of the context vector into an intent vector with an affinity engine;

wherein said affinity engine learns connections between documents and terms, documents and documents, terms and terms, as well as users to other users, documents, and terms by watching all implicit behaviors of a user community on a Web site and by applying behavioral fingerprinting and a use rank algorithm to determine interest and associations.

16. The apparatus of claim 15, further comprising:

means for identifying a peer group of users who share affinity to a current intent, as well as those users who exhibit behavior most like the current user within the context of that intent, said peer group being represented by a user vector, wherein each user entry in said user vector may have a weight indicating how strong of a peer he is to the current user in a current context.

17. The apparatus of claim 16, further comprising:

means for looking at which documents have highest affinity to identified peers within a current intent;

wherein said affinity engine looks at an activeness vector, term-doc matrices, and next-step matrices associated with those peers, weighted according to their peer weight, and uses these in aggregate to computes those documents with highest affinity to the current intent; and

wherein said documents are unfiltered recommendations.

18. The apparatus of claim 17, further comprising:

means for asking said affinity engine for community information on each of said recommendations;

wherein said information is combined with other asset information and displayed to the user to help him understand the community wisdom underlying a recommendation.

19. The apparatus of claim 18, wherein said displayed information comprises any of a number of users in total who found value in a document, a number of peers who associated a document with a current context and/or intent, and terms and/or phrases the peer community has associated with a document.

20. The apparatus of claim 19, wherein said terms and/or phrases comprise a virtual folksonomy that represents terms that the community has associated to a document;

wherein said virtual folksonomy is created automatically by said affinity engine based on implicit actions of the user community.