Abstract: The present invention provides a method and system for determining related bid terms. The method and system includes accessing a term database to determine a plurality of term pairs, the term pairs being paired terms bidded together in a term bidding operating environment. In the method and system, for each of the plurality of term pairs, the method and system includes determining similarity values for each of the term pairs. The method and system further includes generating a similarity matrix using the determined similarity values. And, the method and system includes generating an output result based on a co-bidded relationship between at least one of the terms and advertising information.

Abstract: Methods and systems are provided that may be used to determine a probability of whether a visitor to a web document is likely to click on a web advertisement. An exemplary method may include detecting one or more features in a web document. One or more expert statistical models to which the web document belongs may be determined and associated weightings may be determined based, at least in part, on the one or more features detected. A click-through-rate probability for a web advertisement to be placed on the web document may be estimated based on the one or more expert statistical models.

Abstract: The subject matter disclosed herein relates to returning cached object results based at least in part on a non-exact comparison with a query key.

Abstract: Methods for selecting one or more advertisements based on previously captured relevance data to serve to a client system requesting a primary webpage is provided. The client displays a referring webpage having a hyperlink to the primary webpage. Upon selection of the hyperlink, the client sends a request to a content server storing the primary webpage. The content server classifies the primary webpage for content and retrieves persistent relevance information, possibly including a referrer of the primary webpage comprising a URL address of the referring webpage, a listing of other recently visited webpages, a listing of any bid phrases from previously displayed advertisements, and a listing of recent click data. The content server sends the primary webpage to the client, which includes an advertisement server request. The transaction between the content server and the advertisement server includes persistence relevance information to select advertisements to serve to the client.

Abstract: A computer-implemented method is disclosed for determining a type of landing page to which to transfer web searchers that enter a particular query, the method comprising: classifying a landing page as one of a plurality of landing page classes with a trained classifier of a computer based on textual content of the landing page; determining, by the computer, characteristics of one or more query to be associated with the landing page; and choosing, with the computer, whether to retain or to change classification of the landing page to be associated with the one or more query based on relative average conversion rates of advertisements on a plurality of manually-classified landing pages when associated with the characteristics of the one or more query.

Abstract: The subject matter disclosed herein relates to cross-lingual query classification.

Abstract: Methods and systems are described for enhancing the difficulty of captchas and enlarging a core of available captchas that are hard for an automated or robotic user to crack.

Abstract: Methods and systems are described for generating captchas and enlarging a core of available captchas that are hard for an automated or robotic user to crack.

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: 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: 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: 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 for estimating an amount of traffic associated with a digital ad are disclosed. Generally, a forecasting module identifies a set of candidate webpages on which a digital ad may be displayed and estimates a click through rate associated with the digital ad and a webpage of the set of candidate webpages. The forecasting module determines a ranking score associated with the digital ad based on the determined click through rate and a bid price associated with the digital ad. The forecasting module then examines historical data, such as search logs, to determine an estimate of traffic associated with the digital ad with respect to the webpage in response to determining the ranking score of the digital ad exceeds a ranking score associated with another digital ad that was previously displayed on the webpage.

Abstract: Systems and methods are herein disclosed for assessing the staleness of a web page. In particular, in one method of the present invention, the staleness of a web page is assessed by examining internal date references within the web page. In another method of the present invention, the staleness of a web page is assessed by examining the meta-data associated with the web page. In a further method of the present invention, the staleness of a hyperlinked web page is determined by examining the link status of the hyperlinks. If the web page has a relatively large number of dead links, it is assessed as being a stale web page. In a still further method of the present invention, the link status of web pages in the neighborhood of the web page being assessed is likewise examined.

Abstract: Systems and methods are herein disclosed for assessing the staleness of a web page. In particular, in one method of the present invention, the staleness of a web page is assessed by examining internal date references within the web page. In another method of the present invention, the staleness of a web page is assessed by examining the meta-data associated with the web page. In a further method of the present invention, the staleness of a hyperlinked web page is determined by examining the link status of the hyperlinks. If the web page has a relatively large number of dead links, it is assessed as being a stale web page. In a still further method of the present invention, the link status of web pages in the neighborhood of the web page being assessed is likewise examined.

Abstract: Systems and methods are herein disclosed for assessing the staleness of a web page. In particular, in one method of the present invention, the staleness of a web page is assessed by examining internal date references within the web page. In another method of the present invention, the staleness of a web page is assessed by examining the meta-data associated with the web page. In a further method of the present invention, the staleness of a hyperlinked web page is determined by examining the link status of the hyperlinks. If the web page has a relatively large number of dead links, it is assessed as being a stale web page. In a still further method of the present invention, the link status of web pages in the neighborhood of the web page being assessed is likewise examined.

Abstract: Disclosed is a system architecture, components and a searching technique for an Unstructured Information Management System (UIMS). The UIMS may be provided as middleware for the effective management and interchange of unstructured information over a wide array of information sources. The architecture generally includes a search engine, data storage, analysis engines containing pipelined document annotators and various adapters. The searching technique makes use of a two-level searching technique. A search query includes a search operator containing of a plurality of search sub-expressions each having an associated weight value. The search engine returns a document or documents having a weight value sum that exceeds a threshold weight value sum. The search operator is implemented as a Boolean predicate that functions as a Weighted AND (WAND).

Abstract: A system and method are disclosed for selecting a resource, among a plurality of resources, for servicing a request. To select a resource to service the request, a first resource is randomly selected. If a first load value associated with the first resource does not exceed a threshold value, the request is assigned to the first resource for servicing. Otherwise a second resource is randomly selected. If a second load value associated with the second resource does not exceed a threshold value, the request is assigned to the second resource for servicing. If the second load value exceeds the threshold value, the request is assigned whichever of the first and second resources has a lower load value.