System and method for automated mapping of keywords and key phrases to documents
First Claim
1. A system for selecting an advertisement for display with a web page requested by a user through a web browser, the system comprising:
- at least one server, in communication with said web browser, configured to;
receive, as input, a plurality of documents and a plurality of key terms;
analyze content of the plurality of documents;
automatically generate a key term feature vector for each key term based on the content of the plurality of documents, each key term feature vector comprising elements comprising a plurality of words or phrases that are related to the corresponding key term;
analyze at least one of a Universal Resource Locator (“
URL”
) of said web page and a content of said web page;
automatically generate a document feature vector based on the URL or the content of the web page, the document feature vector comprising elements comprising a plurality of words or phrases that are contained in the URL or content of the web page;
compare the key term feature vector and the document feature vector;
generate a relevance factor based on the similarity of the elements of the key term feature vector and the document feature vector; and
provide a relevant advertisement for display by said web browser with said web page according to the determined relevance factor.
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Abstract
A method for automated mapping of key terms to documents. Preferably a feature vector is generated for the key terms. Preferably such feature vectors are automatically generated by analyzing a corpus, but may optionally be generated manually, or using a combination of automated and manual processes. Next, preferably such feature vectors are weighted. Such weighting may optionally be performed manually, but more preferably is performed automatically. Next, a feature vector is optionally and preferably generated for the document, which preferably includes words and phrases that were extracted from the document but may optionally include words and phrases that do not appear in the document, such as synonyms and related. Each element in the document feature vector is preferably weighted. The feature vectors of the key terms and the feature vectors of the documents are compared, in order to produce relevancy scores, which are used to produce mapping between documents and key terms. This mapping may optionally be used for a wide variety of applications, such as for targeted advertising for example.
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Citations
32 Claims
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1. A system for selecting an advertisement for display with a web page requested by a user through a web browser, the system comprising:
at least one server, in communication with said web browser, configured to; receive, as input, a plurality of documents and a plurality of key terms; analyze content of the plurality of documents; automatically generate a key term feature vector for each key term based on the content of the plurality of documents, each key term feature vector comprising elements comprising a plurality of words or phrases that are related to the corresponding key term; analyze at least one of a Universal Resource Locator (“
URL”
) of said web page and a content of said web page;automatically generate a document feature vector based on the URL or the content of the web page, the document feature vector comprising elements comprising a plurality of words or phrases that are contained in the URL or content of the web page; compare the key term feature vector and the document feature vector; generate a relevance factor based on the similarity of the elements of the key term feature vector and the document feature vector; and provide a relevant advertisement for display by said web browser with said web page according to the determined relevance factor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A method for selecting advertising for display with a web page requested by a user comprising:
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receiving, as input, a plurality of documents and a plurality of key terms; analyzing content of the plurality of documents; automatically generating a key term feature vector for each key term based on the content of the plurality of documents, each key term feature vector comprising elements comprising a plurality of words or phrases that are related to the corresponding key term; analyzing at least one of a Universal Resource Locator (“
URL”
) of said web page and a content of said web page;automatically generating a document feature vector based on the URL or the content of the web page, the document feature vector comprising elements comprising a plurality of words or phrases that are contained in the URL or content of the web page; comparing the key term feature vector and the document feature vector; generating a relevance factor based on the similarity of the elements of the key term feature vector and the document feature vector; and providing a relevant advertisement for display by said web browser with said web page according to the determined relevance factor. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32)
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Specification