Recommendation systems and methods using interest correlation

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.

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.

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.

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.