Recommendation systems and methods using interest correlation

• US 8,122,047 B2
• Filed: 05/17/2010
• Issued: 02/21/2012
• Est. Priority Date: 05/25/2007
• Status: Active Grant

• Current Assignee: PRJ Holding Company LLC
• Original Assignee: Piksel Incorporated
• Inventors: Kanigsberg, Issar Amit, Veidlinger, Daniel Marc, El Shazli, Tamer, Mozersky, Myer Joshua
• Primary Examiner(s): Fleurantin, Jean B
• Application Number:
  US12/781,542
• Publication Number:
  US 20100318423A1
• Time in Patent Office: 645 Days
• Field of Search: 707600-831
• US Class Current: 707/765
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0625 Directed, with specific intent or strategy
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

A search technology generates recommendations with minimal user data and participation, and provides better interpretation of user data, such as popularity, thus obtaining breadth and quality in recommendations. It is sensitive to the semantic content of natural language terms and lets users briefly describe the intended recipient (i.e., interests, eccentricities, previously successful gifts). Based on that input, the recommendation software system and method determines the meaning of the entered terms and creatively discover connections to gift recommendations from the vast array of possibilities. The user may then make a selection from these recommendations. The search/recommendation engine allows the user to find gifts through connections that are not limited to previously available information on the Internet. Thus, interests can be connected to buying behavior by relating terms to respective items.

Claims

1. A computer implemented method of recommending products and services comprising the digital processing steps of:

processing a plurality of web-based user profiles generated by users to extract keywords;

identifying which keywords commonly occur together in at least a portion of the plurality of web-based user profiles including;

weighing the importance of an identified keyword to a subject user profile, where weighing the importance of an identified keyword to a subject user profile further includes increasing the importance proportionally to the number of times the identified keyword appears in the subject user profile offset by the frequency the identified keyword appears in the corpus of user profiles; and

determining a percentage of co-occurrence for the identified keyword using a correlation index; and

expanding keywords associated with a query with additional keywords, where the additional keywords are determined using one or more of the identified co-occurring keywords that commonly occur together in at least a portion of the plurality of web-based user profiles.

2. A computer implemented method as in claim 1 wherein identifying which keywords commonly occur together in the same user profiles further includes computing the frequency with which a keyword appears in conjunction with another keyword in at least a portion of the plurality of web-based user profiles.

3. A computer implemented method as in claim 2 wherein computing the frequency with which a keyword appears in conjunction with another keyword in at least a portion of the plurality of web-based user profiles further includes:

computing the degree to which the two keywords tend to occur together;

determining a ratio indicating the frequency with which the two keywords appear together; and

determining a correlation index indicating the likelihood that users interested in one of the keywords will be interested in the other keyword, as compared to an average profile.

4. A computer implemented method as in claim 3 further including processing the computed degree, the determined ratio and the correlation index to determine a percentage of co-occurrence for each keyword.

5. A computer implemented method as in claim 4 wherein determining a percentage of co-occurrence for each keyword further includes using the percentage of co-occurrence to determine a correlation ratio indicating how often a co-occurring keyword is present when another co-occurring keyword is present.

6. A computer implemented method as in claim 1 wherein weighing the importance of an identified keyword to a subject user profile further includes using a scoring system employing a topic vector space model to produce relevancy vector space of related keywords.

7. A computer implemented method as in claim 6 wherein crawling user profiles further includes:

analyzing user profiles; and

extracting keywords from the analyzed user profiles.

8. A computer implemented method as in claim 1 wherein weighing the importance of an identified keyword to a subject user profile further includes using a term frequency—

inverse document frequency (idf) weighting calculation to determine the value of the identified keyword as an indication of user interest.

9. A computer implemented method as in claim 8 further includes determining whether the identified keyword is a super node.

10. A computer implemented method as in claim 9 wherein a super node is a classifier that is identified by determining the overall frequency of its occurrence in the corpus of user profiles.

11. A computer implemented method as in claim 9 wherein determining whether the identified keyword is a super node further includes determining that the identified keyword is not a super node if the idf value of the identified keyword is below zero.

12. A computer implemented method as in claim 1 wherein the query includes the keywords to be expanded, where the keywords to be expanded describe targeting criteria for an advertisement.

13. A computer implemented method as in claim 12 wherein the advertisement is delivered in response to a user having one or more keywords in their profile that are the same as the keywords associated with the advertisement.

14. A computer implemented method as in claim 1 wherein processing the web-based user profiles further includes crawling user generated content from the user profiles on a social networking site to extract keywords.

15. A computer implemented method as in claim 1 wherein other online information is processed to identify keywords commonly occurring together, where the online information includes consumer buying behavior.

16. A computer apparatus embodied on a nontransitory computer readable storage medium for recommending products and services comprising:

means for processing, using one or more computer processors, a plurality of web-based user profiles generated by users to extract keywords;

means for identifying which keywords commonly occur together in at least a portion of the plurality of web-based user profiles;

wherein the means for identifying keywords that commonly occur together in the same user profiles further including means for weighing the importance of an identified keyword to a subject user profile, where weighing the importance of an identified keyword to a subject user profile further includes increasing the importance proportionally to the number of times the identified keyword appears in the subject user profile offset by the frequency the identified keyword appears in the corpus of user profiles; and

wherein the means for identifying keywords that commonly occur together in the same user profiles further including means for determining a percentage of co-occurrence for the identified keyword using a correlation index; and

means for expanding keywords associated with a query with additional keywords, where the additional keywords are determined using one or more of the identified co-occurring keywords that commonly occur together in at least a portion of the plurality of web-based user profiles.

17. A computer apparatus embodied on a nontransitory computer readable storage medium for recommending products and services comprising:

means for processing, using one or more computer processors, a plurality of web-based user profiles generated by users to extract keywords;

means for identifying which keywords commonly occur together in at least a portion of the plurality of web-based user profiles including means for weighing the importance of an identified keyword to a subject user profile, where weighing the importance of an identified keyword to a subject user profile further includes using a scoring system employing a topic vector space model to produce relevancy vector space of related keywords; and

wherein the means for identifying keywords that commonly occur together in the same user profiles further including means for determining a percentage of co-occurrence for the identified keyword using a correlation index; and

means for expanding keywords associated with a query with additional keywords, where the additional keywords are determined using one or more of the identified co-occurring keywords that commonly occur together in at least a portion of the plurality of web-based user profiles.