Abstract: Dynamic content can be delivered via email. Email messages include HTML content with one or more hyperlinks referring to a network-connected dynamic content server. The hyperlinks are accessed upon rendering of the email on a client device, triggering a request to a dynamic content server. The dynamic content server determines a content item to be served in response to the request. The dynamic content item determination may be based on factors including the identity of the requester, the email communication in which the hyperlink is embedded, recent activities or behavior on the part of the requester, and/or known preference or demographic information associated with the requester. The request can be directed to a selected content item stored within a content delivery network, for transmission back to the requester and display to the user within the email.

Abstract: Dynamic content can be delivered via email. Email messages include HTML content with one or more hyperlinks referring to a network-connected dynamic content server. The hyperlinks are accessed upon rendering of the email on a client device, triggering a request to a dynamic content server. The dynamic content server determines a content item to be served in response to the request. The dynamic content item determination may be based on factors including the identity of the requester, the email communication in which the hyperlink is embedded, recent activities or behavior on the part of the requester, and/or known preference or demographic information associated with the requester. The request can be directed to a selected content item stored within a content delivery network, for transmission back to the requester and display to the user within the email.

Abstract: A computer-implemented method for generating content recommendations for content items, each content item associated with one of a plurality of customers, the method comprising: receiving, by a network-connected server, a content request from a requesting user, the content request comprising a user identifier and a customer identifier; retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of a plurality of models and parameters for the models; retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier; routing the content request, request parameters and user data to a plurality of ranking and optimization component, each component generating a recommendation score for each recommendable resource; generating content recommendations based on the recommendation score which content recommendations are returned to the requesting user and stored by the server as a recommendation event within a r

Abstract: Processes and apparatuses for content personalization are provided, providing for rule configuration. Content personalization systems interpret user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. Rules include filters applied to the predictive model output and/or the overall system output. Filters can be prioritized and iteratively applied or removed to adjust system output to satisfy desired result set criteria.

Abstract: A computer-implemented method for generating content recommendations for content items, each content item associated with one of a plurality of customers, the method comprising: receiving, by a network-connected server, a content request from a requesting user, the content request comprising a user identifier and a customer identifier; retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of a plurality of models and parameters for the models; retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier; routing the content request, request parameters and user data to a plurality of ranking and optimization component, each component generating a recommendation score for each recommendable resource; generating content recommendations based on the recommendation score which content recommendations are returned to the requesting user and stored by the server as a recommendation event within a r

Abstract: A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model

Abstract: A computer-implemented method for generating content recommendations for content items, each content item associated with one of a plurality of customers, the method comprising: receiving, by a network-connected server, a content request from a requesting user, the content request comprising a user identifier and a customer identifier; retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of a plurality of models and parameters for the models; retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier; routing the content request, request parameters and user data to a plurality of ranking and optimization component, each component generating a recommendation score for each recommendable resource; generating content recommendations based on the recommendation score which content recommendations are returned to the requesting user and stored by the server as a recommendation event within a r

Abstract: A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model

Abstract: A personalization platform is provided, which interprets user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. An extensible and highly flexible framework can be implemented. In some embodiments, when calculating for a single user, recent behavior is scored against an ensemble of analytic models and the totals are amalgamated into a final recommendation. Any combination of analytic models may be explored and tested in a multivariate framework on this final ranking function. Models may be added and removed on a per-application basis.

Abstract: In some examples, a computer-implemented method for generating content recommendations for content items is provided. Each content item is associated with one of a plurality of customers. An example method may include receiving a content request from a requesting user, the content request comprising a user identifier and a customer identifier, and retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of one or more recommendation strategies, and parameters for the recommendation strategies. The method may also include retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier, and routing the content request, request parameters and user data to a plurality of computer-implemented scorers, each scorer generating a recommendation score for each recommendable resource.

Abstract: Embodiments are directed to generating and managing an advertising campaign based on a third-party sales listing. In particular, an identifier associated with a sales listing posted over a third-party service from a user of the third-party service is received. Sales listing data for the sales listing is retrieved from the third-party service using the identifier associated with the sales listing. Thereafter, an advertising campaign is automatically generated based at least in part on the retrieved sales listing data. In one aspect, generation of the advertising campaign includes generating an advertising creative using the retrieved sales listing data. Subsequently, bidding on an advertising opportunity in an auction is performed according to the generated advertising campaign. Responsive to winning the auction, an advertisement based on the advertising creative is provided to a content provider associated with the advertising opportunity.

Abstract: Processes and apparatuses for content personalization are provided, providing for rule configuration. Content personalization systems interpret user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. Rules include filters applied to the predictive model output and/or the overall system output. Filters can be prioritized and iteratively applied or removed to adjust system output to satisfy desired result set criteria.

Abstract: Dynamic content can be delivered via email. Email messages include HTML content with one or more hyperlinks referring to a network-connected dynamic content server. The hyperlinks are accessed upon rendering of the email on a client device, triggering a request to a dynamic content server. The dynamic content server determines a content item to be served in response to the request. The dynamic content item determination may be based on factors including the identity of the requester, the email communication in which the hyperlink is embedded, recent activities or behavior on the part of the requester, and/or known preference or demographic information associated with the requester. The request can be directed to a selected content item stored within a content delivery network, for transmission back to the requester and display to the user within the email.

Abstract: Embodiments are directed to generating and managing an advertising campaign based on a third-party listing. In particular, an identifier associated with a listing posted over a third-party service from a user of the third-party service is received. Listing data for the listing is retrieved from the third-party service using the identifier associated with the listing. Thereafter, an advertising campaign is automatically generated based at least in part on the retrieved listing data. In one aspect, generation of the advertising campaign includes generating an advertising creative using the retrieved listing data. Subsequently, an advertising opportunity is selected according to the generated advertising campaign. Following the selection, an advertisement based on the advertising creative is provided to a content provider associated with the advertising opportunity.

Abstract: Methods, systems, and computer program products are described that enable access to resources, some of the resources being related to a degree to each other. A metric is measured that is associated with the access, and information is stored that describes the access. A data structure is generated that represents the resources, the access to the resources, and the respective degrees of relationship among the resources. Based on the data structure, an allocation priority for a resource is generated, and the resource is allocated based on the allocation priority.

Abstract: A data structure for representing metadata describes the content of at least one shot or sub-shot of information material. The data structure comprises a volume identification defining a data carrier on which the information material is contained, at least one shot identification defining the shot or sub-shot within the material, and at least one type of metadata associated with the content of the shot or sub-shot. The volume identification is arranged at a first hierarchical level and the shot identification is arranged at a lower hierarchical level and the metadata describing the shot is arranged with respect to the shot hierarchical level. The data structure provides an efficient facility for communicating metadata between metadata nodes including application programs which use the metadata. The metadata is identified on the basis of a shot and volume on which the information material which the metadata describes is contained.

Abstract: A system for facilitating production of information material comprises a plurality of metadata nodes operable to process metadata describing the content of information material, and a data communications network for providing a facility for communicating the metadata between the metadata nodes. The metadata is represented as metadata strings describing the content or attributes associated with the information material. The metadata string includes a Uniform Resource Identifier (URI) providing an address of a storage location of an associated metadata resource. The metadata string is communicable separately from the information material via the network and the associated metadata resource is accessible via the network using the resource identifier. The metadata string can be arranged to provide an indication of parameters describing the information material and identifiers such as UMIDs or the like which facilitate navigation of the information material.

Abstract: A data structure for representing metadata describes the content of at least one shot or sub-shot of information material. The data structure comprises a volume identification defining a data carrier on which the information material is contained, at least one shot identification defining the shot or sub-shot within the material, and at least one type of metadata associated with the content of the shot or sub-shot. The volume identification is arranged at a first hierarchical level and the shot identification is arranged at a lower hierarchical level and the metadata describing the shot is arranged with respect to the shot hierarchical level. The data structure provides an efficient facility for communicating metadata between metadata nodes including application programs which use the metadata. The metadata is identified on the basis of a shot and volume on which the information material which the metadata describes is contained.

Abstract: A system for facilitating production of information material comprises a plurality of metadata nodes operable to process metadata describing the content of information material, and a data communications network for providing a facility for communicating the metadata between the metadata nodes. The metadata is represented as metadata strings describing the content or attributes associated with the information material. The metadata string includes a Uniform Resource Identifier (URI) providing an address of a storage location of an associated metadata resource. The metadata string is communicable separately from the information material via the network and the associated metadata resource is accessible via the network using the resource identifier. The metadata string can be arranged to provide an indication of parameters describing the information material and identifiers such as UMIDs or the like which facilitate navigation of the information material.