Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more computer processors and perform configuring one or more advertisements for a sponsored product customized to fit a page type associated with a page format of a website; analyzing one or more inputs to create merged data; generating a respective rank of the advertisements of the sponsored product using a conversion probability; adjusting the respective rank the one or more advertisements of the sponsored product as a function of the merged data; automatically designating one of the one or more advertisements of the sponsored product that is ranked higher than other advertisements to be positioned closest to a predefined portion of a webpage; and sending instructions to display the one of the one or more advertisements closest to the predefined portion of the webpage of the website. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of collecting training attribute values for a set of attributes; generating a probability model using the training attribute values, the probability model providing predicted outcomes for at least one attribute in the set of attributes; identifying, using the probability model, a first predicted outcome corresponding to a subsequent combination of attribute values collected by a collection module, wherein the predicted outcomes can comprise the first predicted outcome; coordinating a first display of an ad content version of a subsequent webpage when the first predicted outcome satisfies an objective of the subsequent webpage, wherein the first display can comprise: (a) an ad at a first location on the subsequent webpage and a webpage content in a first format at a second location on the subsequent we

Abstract: A system and method includes configuring a computer system to provide output of one or more advertisements of products to a user device of a consumer, receiving, by the computer system, one or more inputs of impressions, clicks, orders and other KPIs, estimating, by the computer system, for a click through rate (CTR) of advertisements of the products based on the inputs received, optimizing, by the computer system with an optimization module, an estimated revenue and estimated advertisement spending (Adspend) based on the CTR of advertisements, and providing, by the computer system, output of one or more advertisements of the products to the user device of the consumer for display.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more computer processors and perform configuring one or more advertisements for a sponsored product customized to fit a page type associated with a page format of a website; analyzing one or more inputs to create merged data; generating a respective rank of the advertisements of the sponsored product using a conversion probability; adjusting the respective rank the one or more advertisements of the sponsored product as a function of the merged data; automatically designating one of the one or more advertisements of the sponsored product that is ranked higher than other advertisements to be positioned closest to a predefined portion of a webpage; and sending instructions to display the one of the one or more advertisements closest to the predefined portion of the webpage of the website. Other embodiments are disclosed.

Abstract: A system and method includes receiving, by a computer system, one or more inputs of customer level information, advertisement page and page type level information, item level information, inventory constraints, competitor pricing, and seasonal requirements/rules, merging, by the computer system, the one or more inputs, receiving, by the computer system including a module selection and ranking engine, the merged data, and providing, by the computer system, output of one or more advertisements of sponsored product listings to a user device of a consumer for display.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of collecting training attribute values for a set of attributes; generating a probability model using the training attribute values, the probability model providing predicted outcomes for at least one attribute in the set of attributes; identifying, using the probability model, a first predicted outcome corresponding to a subsequent combination of attribute values collected by a collection module, wherein the predicted outcomes can comprise the first predicted outcome; coordinating a first display of an ad content version of a subsequent webpage when the first predicted outcome satisfies an objective of the subsequent webpage, wherein the first display can comprise: (a) an ad at a first location on the subsequent webpage and a webpage content in a first format at a second location on the subsequent we

Abstract: Systems and methods are disclosed for managing the display of online ad content consistent with one or more e-commerce objectives. A collection module may be operable to collect attribute values for a set of attributes characterizing user visits to a set of training webpages and subsequent attribute values for a subsequent user visit to a subsequent webpage. A model-generation module may be operable to train a probability model with the attribute values that predicts outcomes for at least one performance metric. A display module may be operable to determine whether to display ad content on the subsequent webpage for the subsequent user visit depending on whether a predicted outcome from the probability model that is relevant to the subsequent attribute values is consistent with one or more e-commerce objectives. The probability model may be a decision tree with different predicted outcomes for different combinations of attribute values.

Abstract: A method of establishing personalized limits on a search responsive to a key word query in an enterprise search system is described that includes receiving an object types access history for a particular user. Applying this method, the object types access history includes records of object types selected from search results returning multiple object types and records of object types selected via interfaces other than search results. The method continues with determining and storing in computer readable memory a personalized scope of object types. The personalized scope of object types sets a limit on object types initially returned by an enterprise search system for the particular user in response to key word queries by the particular user that do not specify object types to search.

Abstract: Systems and methods are disclosed herein for supplementing product records with product groups that are relevant to the product records. Queries form users may be analyzed to extract keywords. Search results for keywords are evaluated to determine category consistency among product records, including such values as entropy and taxonomy depth. Those keywords with search results having adequate category consistency are selected as product groups and the search results associated with the product groups. Product groups are associated with product records according to a random walk of a graph having as nodes products and product groups and links representing belonging of a product to a product group. Product groups may be selected based on a transition probability based on a random walk and a quality score based on usage of a product group page for the product group.

Abstract: A method of extending an existing product taxonomy for an inventory of products. The existing product taxonomy can include pages having a hierarchical tree structure. The method can include determining a first set of keywords. The first set of keywords can be a prediction of keywords searched above a predetermined threshold during a predetermined time period of one or more external search engines. The method also can include generating dynamic product groups based on the first set of keywords. The method further can include generating a relation graph of the dynamic product groups. The relation graph can include relation links between the dynamic product groups. The method also can include linking the dynamic product groups to pages in the existing product taxonomy to generate an extended product taxonomy. The extended product taxonomy can include the existing product taxonomy, the dynamic product groups, and the relation links of the relation graph between the dynamic product groups.

Abstract: A system and method includes receiving, by a computer system, one or more inputs of customer level information, advertisement page and page type level information, item level information, inventory constraints, competitor pricing, and seasonal requirements/rules, merging, by the computer system, the one or more inputs, receiving, by the computer system including a module selection and ranking engine, the merged data, and providing, by the computer system, output of one or more advertisements of sponsored product listings to a user device of a consumer for display.

Abstract: A system and method includes configuring a computer system to provide output of one or more advertisements of products to a user device of a consumer, receiving, by the computer system, one or more inputs of impressions, clicks, orders and other KPIs, estimating, by the computer system, for a click through rate (CTR) of advertisements of the products based on the inputs received, optimizing, by the computer system with an optimization module, an estimated revenue and estimated advertisement spending (Adspend) based on the CTR of advertisements, and providing, by the computer system, output of one or more advertisements of the products to the user device of the consumer for display.

Abstract: A method of extending an existing product taxonomy for an inventory of products. The existing product taxonomy can include pages having a hierarchical tree structure. The method can include determining a first set of keywords. The first set of keywords can be a prediction of keywords searched above a predetermined threshold during a predetermined time period of one or more external search engines. The method also can include generating dynamic product groups based on the first set of keywords. The method further can include generating a relation graph of the dynamic product groups. The relation graph can include relation links between the dynamic product groups. The method also can include linking the dynamic product groups to pages in the existing product taxonomy to generate an extended product taxonomy. The extended product taxonomy can include the existing product taxonomy, the dynamic product groups, and the relation links of the relation graph between the dynamic product groups.

Abstract: Systems and methods are disclosed for managing the display of online ad content consistent with one or more e-commerce objectives. A collection module may be operable to collect attribute values for a set of attributes characterizing user visits to a set of training webpages and subsequent attribute values for a subsequent user visit to a subsequent webpage. A model-generation module may be operable to train a probability model with the attribute values that predicts outcomes for at least one performance metric. A display module may be operable to determine whether to display ad content on the subsequent webpage for the subsequent user visit depending on whether a predicted outcome from the probability model that is relevant to the subsequent attribute values is consistent with one or more e-commerce objectives. The probability model may be a decision tree with different predicted outcomes for different combinations of attribute values.

Abstract: Systems and methods are disclosed herein for supplementing product records with product groups that are relevant to the product records. Queries form users may be analyzed to extract keywords. Search results for keywords are evaluated to determine category consistency among product records, including such values as entropy and taxonomy depth. Those keywords with search results having adequate category consistency are selected as product groups and the search results associated with the product groups. Product groups are associated with product records according to a random walk of a graph having as nodes products and product groups and links representing belonging of a product to a product group. Product groups may be selected based on a transition probability based on a random walk and a quality score based on usage of a product group page for the product group.

Abstract: Systems and methods are disclosed herein for supplementing product records with product groups that are relevant to the product records. Queries form users may be analyzed to extract keywords. Search results for keywords are evaluated to determine category consistency among product records, including such values as entropy and taxonomy depth. Those keywords with search results having adequate category consistency are selected as product groups and the search results associated with the product groups. Product groups are associated with product records according to a random walk of a graph having as nodes products and product groups and links representing belonging of a product to a product group. Product groups may be selected based on a transition probability based on a random walk and a quality score based on usage of a product group page for the product group.

Abstract: A method of establishing personalized limits on a search responsive to a key word query in an enterprise search system is described that includes receiving an object types access history for a particular user. Applying this method, the object types access history includes records of object types selected from search results returning multiple object types and records of object types selected via interfaces other than search results. The method continues with determining and storing in computer readable memory a personalized scope of object types. The personalized scope of object types sets a limit on object types initially returned by an enterprise search system for the particular user in response to key word queries by the particular user that do not specify object types to search.

Abstract: In one embodiment, a computer-implemented method executable by a server system to establish suggested queries is provided. The method comprises: receiving an initial query; evaluating a user click log for at least one query that is similar to the initial query based on a document click count of results generated from the at least one query; and generating at least one suggested query based on the at least one query that is similar to the initial query.

Abstract: A method of establishing personalized limits on a search responsive to a key word query in an enterprise search system is described that includes receiving an object types access history for a particular user. Applying this method, the object types access history includes records of object types selected from search results returning multiple object types and records of object types selected via interfaces other than search results. The method continues with determining and storing in computer readable memory a personalized scope of object types. The personalized scope of object types sets a limit on object types initially returned by an enterprise search system for the particular user in response to key word queries by the particular user that do not specify object types to search.