Abstract: A method and system are provided for performing causal inferencing in a computationally efficient manner. In one embodiment, a computer-implemented method includes collecting user interaction data for a plurality of users, within a specified observation window. The collected data comprises a treatment observation for at least one user and an outcome observation for at least one user. Memory for a feature table is allocated, wherein a size the allocated memory is proportional to a number of features in the collected data. Feature-related values are stored in the feature table based on respective pre-treatment observation periods for each of the plurality of users. A selected number of confounders are identified from the feature table. An effect of the treatment is computed on the outcome using the selected confounders.

Abstract: Methods, apparatuses, and articles for receiving one or more indicators indicating one or more degrees of relevance or irrelevance of one or more items or entities to advertising keywords of interest, generating one or more advertising keyword suggestions suggesting one or more advertising keywords based at least in part on whether there are predetermined relationships among the suggested one or more advertising keywords, and between some of the suggested one or more advertising keywords and the one or more items or entities, are described herein.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize search terms (potential search terms) representing companies, based on their respective predicted value to users. The value of a job-related search term is expressed as a priority score assigned to that search term. The SEO system generates priority scores for different search terms, using a probabilistic model that takes into account a value expressing how likely the search term is to be included in a search query, as well as other signals that are indicative of the relative importance of a company represented by the search term.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize search terms (potential search terms) representing geographic locations, based on their respective predicted value to users. The value of a job-related search term is expressed as a priority score assigned to that search term. The SEO system generates priority scores for different search terms, using a probabilistic model that takes into account a value expressing how likely the search term is to be included in a search query, as well as other signals that are indicative of the relative importance of a location represented by the search term.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: A search engine optimization system is provided with an on-line social network system. The on-line social network system includes or is in communication with a search engine optimization (SEO) system that is configured to prioritize keywords (potential search terms) based on their respective predicted contribution to the ranking of JSERPs. The value of a job-related keyword is expressed as a priority score assigned to that keyword. The SEO system generates priority scores for different keywords, using a probabilistic model that takes into account a value expressing how likely the keyword is to be included in a search query as a search term and/or a value expressing how likely is a search that includes the keyword as a search term is to produce relevant results.

Abstract: Methods, apparatuses, and articles of manufacture for generating advertisements using an algorithmic system, such as a combinatoric system, and determining effectiveness metrics or predictions for the advertisements are described herein.

Abstract: Methods, apparatuses, and articles of manufacture for determining optimal selection and/or placement of advertisements in various media objects, based on features of the advertisements and media objects, are described herein.

Abstract: Methods, apparatuses, and articles of manufacture for generating advertisements using an algorithmic system, such as a combinatoric system, and determining effectiveness metrics or predictions for the advertisements are described herein.

Abstract: Methods, apparatuses, and articles for receiving a collection of documents and/or objects determined to be potentially relevant to a keyword, and processing the collection of documents and/or objects to extract one or more keyword characterizations for use as proxies for the keyword, are described herein. In various embodiments, the one or more keyword characterizations may be used to compute a measure of keyword similarity for the keyword, facilitate keyword behavior modeling of the keyword, and/or find one or more advertisements.

Abstract: Methods, apparatuses, and articles of manufacture for detecting, by an agent trusted by a merchant and a search engine, a click on an advertisement displayed by the search engine, are described herein. The trusted agent may further determine that a transaction of the merchant associated with the displayed advertisement was completed and determine whether the completed transaction was resulted from the click. In some embodiments, the trusted agent may notify the merchant and the search engine of a billing event associated with the click, if the completed transaction was determined to be resulted from the click.

Abstract: Methods, apparatuses, and articles of manufacture for generating advertisements using an algorithmic system, such as a combinatoric system, and determining effectiveness metrics or predictions for the advertisements are described herein.

Abstract: Methods, apparatuses, and articles of manufacture for determining optimal selection and/or placement of advertisements in various media objects, based on features of the advertisements and media objects, are described herein.

Abstract: Methods, apparatuses, and articles of manufacture for detecting, by an agent trusted by a merchant and a search engine, a click on an advertisement displayed by the search engine, are described herein. The trusted agent may further determine that a transaction of the merchant associated with the displayed advertisement was completed and determine whether the completed transaction was resulted from the click. In some embodiments, the trusted agent may notify the merchant and the search engine of a billing event associated with the click, if the completed transaction was determined to be resulted from the click.

Abstract: Methods, apparatuses, and articles for receiving a collection of documents and/or objects determined to be potentially relevant to a keyword, and processing the collection of documents and/or objects to extract one or more keyword characterizations for use as proxies for the keyword, are described herein. In various embodiments, the one or more keyword characterizations may be used to compute a measure of keyword similarity for the keyword, facilitate keyword behavior modeling of the keyword, and/or find one or more advertisements.

Abstract: Methods, apparatuses, and articles for computing a predictive measure for an advertising effectiveness metric for the one or more advertising keywords based at least in part on one or more feature values of the keywords employing a prediction function of the effectiveness metric, are described herein. In various embodiments, the prediction function may have been generated based on a plurality of other keywords and feature values of the one or more features of the other keywords.