Abstract: A predictor for determining a degree of relevance between a query rewrite and a search query is provided. The predictor may receive a search query from a user via a terminal and identify a set of candidate query rewrites associated with the search query. The predictor may then extract a set of features from advertisements associated with the query rewrites and the search query and determine a degree of relevance between the advertisements and the search query based on a prediction model. The predictor may then determine the degree of relevance between the rewrites and the search query based on the determined degree of relevance between the advertisements and the search query.

Abstract: The present invention is directed towards a method and system for characterizing web content based on capturing semantics of folksonomies relating to content entities of user generated content. The method and system includes determining a plurality of tags that describe a plurality of content entities and determining a co-occurrence of the tags. The method and system further includes generating weighted vectors based on the determined co-occurrence of tags and characterizing the content entity based on the weight vectors. Thereby, the characterization of the content entity may be used for any number of suitable purposes, including, by way of example, improving search results and associated advertising relevancy.

Abstract: Systems and methods for predicting a degree of relevance between a set of candidate digital ads and a search query are disclosed. Generally, an ad provider receives a digital ad request associated with a search query. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the search query associated with the digital ad request, and determines a degree of relevance between the set of candidate digital ads and the search query based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the search query, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request.

Abstract: Systems and methods for predicting a degree of relevance between a set of candidate digital ads and webpage content are disclosed. Generally, an ad provider receives a digital ad request associated with webpage content. The ad provider identifies a set of candidate digital ads that may be served in response to the digital ad request. A relevance module extracts a set of features from the set of candidate digital ads and the webpage content, and determines a degree of relevance between the set of candidate digital ads and the webpage content based on a prediction model and the extracted set of features. If the relevance module determines the set of candidate digital ads is relevant to the webpage content, the ad provider may serve one or more digital ads from the set of candidate digital ads in response to the received digital ad request.

Abstract: Systems and methods for building a prediction model to predict a degree of relevance between digital ads and a search query or webpage content are disclosed. Generally, an indication of relevance is received between a plurality of digital ads and one of a webpage content or a search query. A set of features is extracted from the plurality of digital ads and one of the webpage content or the search query. A prediction model is then built to predict a degree of relevance between the set of candidate digital ads and one of a second webpage content or a second search query, where the prediction model is built based at least one the received indication of relevance and the extracted set of features.

Abstract: A method is provided to match an advertisement to a search query comprising: receiving search results produced by a search engine in response to a search query; producing an ad query that includes, unigram features, classification features with respect to an external classification system, and phrase features; producing a plurality of representations of corresponding advertisements in terms of the same types of features; and selecting one or more advertisements based upon a measure of similarity of ad query features to advertisements represented in terms of the same features.

Abstract: A system and method are disclosed for rewriting queries. The queries may be rewritten into a bid phrase for identifying search results and/or advertisements. The bid phrase may be a keyword that is purchased for sponsored searching. A mapping between potential queries and bid phrases may be generated. The mapping may be referenced upon receiving a search query for identifying a query rewrite with a bid phrase for that search query. The mapping may be generated in preprocessing.

Abstract: Systems and methods are provided for determining items or people of potential interest to recommend to users in a computer-based network. Implied social networks may be determined based at least in part on obtained social behavior information. Items or people of potential interest to users may be determined based at least in part on implied social network information. Vocabulary taxonomies may be associated with, or used in determining, implied social networks.

Abstract: A system and method to facilitate real-time matching of content to advertising information in a network are described. A request for advertising information is received over a network, the advertising information to be displayed for a user entity in association with content information within a web page requested by the user entity, the request containing the content information, a web page identifier, and additional data associated with the web page. The content information is further analyzed in real-time to construct a page summary of the web page. The web page identifier and the additional data are further analyzed in real-time to extract at least one keyword relevant to the content information. Finally, the advertising information is determined in real-time based on the page summary and the extracted keywords.

Abstract: A method of providing weighted concepts related to a sequence of one or more words, including: providing on a computer an encyclopedia with concepts and a document explaining each concept, forming a vector, which contains the frequency of the word for each concept, for each word in the encyclopedia, arranging the vector according to the frequency of appearance of the word for each concept, selecting the concepts with the highest frequencies for each word from the vector, truncating the rest of the vector, inducing a feature generator using the truncated vectors; wherein the feature generator is adapted to receive as input one or more words and provide a list of weighted concepts, which are most related to the one or more words provided as input.

Abstract: A system and method for categorizing documents with the aid of an external knowledge database. In an exemplary embodiment of the invention, an external knowledge database is used to provide concepts related to the documents of a categorized database and an input document in order to improve the ability of correctly categorizing input documents. Additionally, the above system and method can be implemented to search for documents related to an input document.

Abstract: A system and methodology is provided for filtering temporal streams of information such as news stories by statistical measures of information novelty. Various techniques can be applied to custom tailor news feeds or other types of information based on information that a user has already reviewed. Methods for analyzing information novelty are provided along with a system that personalizes and filters information for users by identifying the novelty of stories in the context of stories they have already reviewed. The system employs novelty-analysis algorithms that represent articles as a bag of words and named entities. The algorithms analyze inter- and intra-document dynamics by considering how information evolves over time from article to article, as well as within individual articles.

Abstract: A system and methodology is provided for filtering temporal streams of information such as news stories by statistical measures of information novelty. Various techniques can be applied to custom tailor news feeds or other types of information based on information that a user has already reviewed. Methods for analyzing information novelty are provided along with a system that personalizes and filters information for users by identifying the novelty of stories in the context of stories they have already reviewed. The system employs novelty-analysis algorithms that represent articles as a bag of words and named entities. The algorithms analyze inter- and intra-document dynamics by considering how information evolves over time from article to article, as well as within individual articles.

Abstract: A method and system for classification, including the steps of searching a data structure including categories for elements related to an input, calculating statistics describing the relevance of each of the elements to the input, ranking the elements by relevance to the input, determining if the ranked elements exceed a threshold confidence value, and returning a set of elements from the ranked elements when the threshold confidence value is exceeded.