Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.

Abstract: An arrangement for providing targeted content includes data repositories storing information from which targeted content may be selected. The data repositories store at least one contextual relationship graph. The arrangement also includes an input/output interface through which a request for targeted content is made. The arrangement further includes a controller that receives the request for targeted content and selects targeted content using the contextual relationship graph. The controller further provides the selected targeted content through the input/output interface.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes receiving recommendation boost instructions, receiving a request for one or more recommendations, receiving a set of recommendations from one or more automated recommendation systems, with each recommendation system utilizing selection models or user models and modifying the set of product recommendations according to the recommendation boost instructions.

Abstract: A system and process for improving product recommendations for a first user includes receiving a request for one or more product recommendations for a first user, each product recommendation being associated with any one of a plurality of retailers, receiving a plurality of recommendation sets from one or more automated product recommendation systems, wherein the plurality of recommendation sets are generated using different selection models and using ensemble learning to select one or more most relevant product recommendation sets from the plurality of product recommendation sets.

Abstract: A system and process for incorporating recommendation boosting in an automated recommendation system includes presenting a user with a visual electronic interface adapted to receive recommendation boost instructions regarding a boost subject, receiving recommendation boost instructions via the visual electronic interface, wherein the recommendation boost instructions indicate how strongly the boost subject should be recommended or suppressed from being recommended, receiving a set of recommendations from one or more automated product recommendation systems, wherein each recommendation system utilizes one or more selection models or user models and modifying the set of recommendations according to the recommendation boost instructions.

Abstract: A method of improving user set recommendations for product advertising including receiving a request for user set recommendations from any of a set of retailers where such request is related to one or more products, receiving from a plurality of user sets from one or more automated user recommendation systems, wherein the plurality of user sets are generated using different user models and using ensemble learning to select one or more most relevant user sets from the plurality of user sets.

Abstract: A process for identifying a subset of users for which a non-competitive advertisement is relevant includes the steps of generating a plurality of user models including user-specific data, identifying a subset of the plurality of user models by applying an advertisement-specific selection model to identify users for which a specific advertisement is relevant and applying a non-competitive rule set to the identified user models to identify which user models are associated with one or more non-competitive originating retailers.

Abstract: A process for identifying a subset of users for which a cooperative electronic advertising is relevant includes the steps of generating user models associated with originating retailers, receiving a request to advertise products related to products sold by the retailers, identifying user models by applying an advertisement specific selection model and communicating the advertising to the identified users such that each user receives a communication apparently sent by the originating retailer.

Abstract: A process for selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements includes the steps of generating a selection model based on product data and user-specific data, generating a user model using the user-specific data and selecting non-competitive personalized electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying advertisements not competitive to the specific retailer.

Abstract: A process for providing cooperative electronic advertising includes the steps of generating a user model associated with an originating retailer, receiving requests to advertise products sold by originating retailers, identifying products by applying advertisement-specific selection models to the user model to identify which of the products is relevant and communicating an advertisement for the products to the user such that the user receives a communication that appears to have been sent by the originating retailer.

Abstract: A process of selecting personalized non-competitive electronic advertising from a plurality of competitive and non-competitive advertisements for electronic display including the steps of generating a selection model, generating a user model, selecting personalized non-competitive electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying non-competitive advertisements and providing in an electronic format identified relevant and non-competitive advertisements. An arrangement for the same includes memory for storing a selection model and a user and a controller that selects personalized non-competitive electronic advertising from the plurality of advertisements using the selection model and user model to identify relevant advertisements and using a rule set for identifying non-competitive advertisements.

Abstract: A process for generating a user model to be used in providing personalized advertisements to a specific user includes the steps of collecting user-specific data while the specific user is interacting with any of a plurality of retailers. The process further includes generating a user model for the specific user utilizing the user-specific data and using the model to generate a personalized advertisement for presentation to the user. An arrangement for generating a user model to be used in providing personalized advertisements to a specific user includes memory for storing user-specific data collected from user interactions with any of a plurality of retailers and a controller that generates a user model for the specific user utilizing the user-specific data and uses the model to generate a personalized advertisement for presentation to one of the users for which user-specific data has been collected.

Abstract: A process for generating a selection model to be used in providing personalized non-competitive advertising including the steps of collecting data from advertising retail websites regarding product data, collecting data from the originating retail websites regarding user behavior, generating a selection model based on the product data and the transactional data and using the selection model to generate personalized non-competitive advertisements for presentation. An arrangement for the same includes memory for storing product data collected from advertising retail websites and data colleted from originating retail websites regarding user behavior and a controller that generates a selection model based on the product data and the transactional data, wherein the selection model includes data sets identifying similar and popular products, and uses the selection model to generate personalized non-competitive advertisements for presentation to one or more of the users for which user behavior has been collected.