Zeta Global Corp.

Other

Wynn Vonnegut

Christian Monberg

Barney Govan

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 repository of recommendation events; evaluating the relative efficacy of the one or more components, which models are returned to the requesting user and stored by the server as a recommendation event within a repository of recommendation events; evaluating the relative efficacy of the one or more components through continued usage; optimizing model efficacy by updating parameters within the parameter service based on the relative efficacy.

Method and apparatus for real-time personalization

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-specific results to generate an ensemble recommendation result; and transmitting the ensemble result from the content recommender to said querying customer content server.

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.

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.

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San Francisco, CA, United States of America

Wynn Vonnegut