Recommendation systems and methods using interest correlation

• US 7,734,641 B2
• Filed: 05/25/2007
• Issued: 06/08/2010
• Est. Priority Date: 05/25/2007
• Status: Active Grant

• Current Assignee: PRJ Holding Company LLC
• Original Assignee: Peerset, Inc. (Piksel Incorporated)
• Inventors: El Shazli, Tamer, Kanigsberg, Issar Amit, Mozersky, Myer Joshua, Veidlinger, Daniel Marc
• Primary Examiner(s): Fleurantin; Jean B
• Application Number:
  US11/807,191
• Publication Number:
  US 20080294621A1
• Time in Patent Office: 1,110 Days
• Field of Search: 707 1- 10, 707100-1041, 707200-206
• 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 user profiles to extract keywords;

identifying which keywords commonly occur together in the same user profiles, where identifying which keywords commonly occur together in the same user profiles includes computing the frequency with which a keyword appears in conjunction with another keyword including;

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;

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; and

determining a percentage of co-occurrence for each keyword, where the percentage of co-occurrence is used to determine a correlation ratio indicating how often a co-occurring keyword is present; and

expanding a search query with additional search terms related to the search query, where the additional search terms are determined using one or more of the identified co-occurring keywords.

2. A computer implemented method as in claim 1 further including processing the computed degree, the determined ratio and the correlation index to determine the percentage of co-occurrence for each keyword.

3. A computer implemented method as in claim 2 wherein the correlation ratio indicates how often a co-occurring keyword is present when another co-occurring keyword is present.

4. A computer implemented method as in claim 1 wherein identifying keywords that commonly occur together in the same user profiles further includes weighing the importance of an identified keyword to a subject user profile.

5. A computer implemented method as in claim 4 wherein 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.

6. A computer implemented method as in claim 4 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 4 weighing the importance of an identified keyword to a subject user profile 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 1 wherein expanding a search query with additional search terms related to the search query further includes receiving, from a search engine, a search query requiring additional search terms.

10. A computer implemented method as in claim 1 wherein the search query is a request for a gift recommendation or a trip recommendation.

11. A computer implemented method as in claim 1 wherein processing user profiles further includes crawling the user profiles on a global computer network to extract keywords.

12. A computer implemented method as in claim 1 wherein the search query is initiated by a user who is shopping online for a product or service.

13. A computer implemented method as in claim 1 wherein expanding a search query with additional search terms related to the search query further includes:

processing advertisement data related to the search query or to the additional search terms; and

causing the advertisement data to be displayed at a computer system of a user that entered the search query.

14. A computer implemented method as in Claim 1 wherein a crawler processes the user profiles to extract keywords.

15. A computer implemented method as in Claim 1 further includes determining whether the identified keyword is a super node.

16. A computer implemented method as in claim 15 wherein a super node is a classifier that is identified by determining the overall frequency of its occurrence in the corpus of user profiles.

17. A computer implemented method as in claim 15 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.

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

processing user profiles to extract keywords;

identifying which keywords commonly occur together in the same 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 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 including;

determining whether the identified keyword is a super node, where the super node is a classifier that is identified by determining the overall frequency of its occurrence in the corpus of user profiles; and

determining that the identified keyword is not a super node if the idf value of the identified keyword is below zero; and

expanding a search query with additional search terms related to the search query, where the additional search terms are determined using one or more of the identified co-occurring keywords.

19. A digital processing system comprising:

one or more computer processors executing computer readable instructions for recommending products and services, the computer readable instructions being executed causing products and services to be recommended at one or more computer systems by;

processing user profiles to extract keywords;

identifying which keywords commonly occur together in the same user profiles, where identifying which keywords commonly occur together in the same user profiles includes weighing the importance of an identified keyword to a subject user profile using a term frequency—

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

determining whether the identified keyword is a super node, where the super node is a classifier that is identified by determining the overall frequency of its occurrence in the corpus of user profiles; and

determining that the identified keyword is not a super node if the idf value of the identified keyword is below zero; and

expanding a search query with additional search terms related to the search query, where the additional search terms are determined using one or more of the identified co-occurring keywords.

20. A computer implemented method of recommending products and services comprising the steps of:

processing user profiles to extract keywords;

identifying which keywords commonly occur together in the same user profiles by computing the frequency with which a keyword appears in conjunction with another keyword including;

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;

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;

processing the computed degree, the determined ratio and the correlation index to determine a percentage of co-occurrence for each keyword;

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; and

determining the keywords that commonly occur together in the same user profiles by identifying the keywords having the highest percentage of co-occurrence; and

expanding a search query with additional search terms related to the search query, where the additional search terms are determined using one or more of the identified co-occurring keywords.

21. A digital processing system comprising:

one or more computer processors executing computer readable instructions for recommending products and services, the computer readable instructions being executed causing products and services to be recommended at one or more computer systems by;

processing user profiles to extract keywords;

identifying which keywords commonly occur together in the same user profiles, where identifying which keywords commonly occur together in the same user profiles includes computing the frequency with which a keyword appears in conjunction with another keyword including;

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;

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; and

determining a percentage of co-occurrence for each keyword, where the percentage of co-occurrence is used to determine a correlation ratio indicating how often a co-occurring keyword is present; and

expanding a search query with additional search terms related to the search query, where the additional search terms are determined using one or more of the identified co-occurring keywords.

22. A computer implemented method as in Claim 21 wherein identifying which keywords commonly occur together in the same user profiles further includes 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 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; and

determining whether the identified keyword is a super node.

23. A computer implemented method as in claim 22 wherein a super node is a classifier that is identified by determining the overall frequency of its occurrence in the corpus of user profiles.

24. A computer implemented method as in claim 22 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.