Abstract: A method of generating digital data content customized for a user of one or more digital data platforms includes determining, through artificial intelligence, interests of a first user of the one or more digital data platforms, as well as those of a plurality of other users. The method further includes generating a product recommendation for the first user by mapping and filtering, e.g., using ontological filtering, natural language processing and/or semantics, the interests determined for the plurality of other users to the interests determined for the first user, and transferring the product recommendation, along with inventory availability, to a client digital data device for presentation to the first user.

Abstract: A scalable recommendation engine includes stateless processors in communication with at least one memory server that stores contextual data. A router is configured to direct a recommendation request to a first stateless processor, which is configured to generate a recommendation using contextual data from the memory server. A controller monitors the available processing bandwidth of the stateless processors and deploys an additional stateless processor if the available processing bandwidth is less than a minimum available processing bandwidth threshold. The controller can remove from deployment a stateless processor if the available processing bandwidth is greater than a maximum pre-determined available processing bandwidth threshold.

Abstract: A method of generating digital data content customized for a user of one or more digital data platforms includes determining, through artificial intelligence, interests of a first user of the one or more digital data platforms, as well as those of a plurality of other users. The method further includes generating a product recommendation for the first user by mapping and filtering, e.g., using ontological filtering, natural language processing and/or semantics, the interests determined for the plurality of other users to the interests determined for the first user, and transferring the product recommendation, along with inventory availability, to a client digital data device for presentation to the first user.

Abstract: A method of generating digital data content customized for a user of one or more digital data platforms includes determining, through artificial intelligence, interests of a first user of the one or more digital data platforms, as well as those of a plurality of other users. The method further includes generating a product recommendation for the first user by mapping and filtering, e.g., using ontological filtering, natural language processing and/or semantics, the interests determined for the plurality of other users to the interests determined for the first user, and transferring the product recommendation, along with inventory availability, to a client digital data device for presentation to the first user.

Abstract: A scalable recommendation engine includes stateless processors in communication with at least one memory server that stores contextual data. A router is configured to direct a recommendation request to a first stateless processor, which is configured to generate a recommendation using contextual data from the memory server. A controller monitors the available processing bandwidth of the stateless processors and deploys an additional stateless processor if the available processing bandwidth is less than a minimum available processing bandwidth threshold. The controller can remove from deployment a stateless processor if the available processing bandwidth is greater than a maximum pre-determined available processing bandwidth threshold.

Abstract: A method of generating digital data content customized for a user of one or more digital data platforms includes determining, through artificial intelligence, interests of a first user of the one or more digital data platforms, as well as those of a plurality of other users. The method further includes generating a product recommendation for the first user by mapping and filtering, e.g., using ontological filtering, natural language processing and/or semantics, the interests determined for the plurality of other users to the interests determined for the first user, and transferring the product recommendation, along with inventory availability, to a client digital data device for presentation to the first user.

Abstract: A scalable recommendation engine includes stateless processors in communication with at least one memory server that stores contextual data. A router is configured to direct a recommendation request to a first stateless processor, which is configured to generate a recommendation using contextual data from the memory server. A controller monitors the available processing bandwidth of the stateless processors and deploys an additional stateless processor if the available processing bandwidth is less than a minimum available processing bandwidth threshold. The controller can remove from deployment a stateless processor if the available processing bandwidth is greater than a maximum pre-determined available processing bandwidth threshold.

Abstract: A scalable recommendation engine includes stateless processors in communication with at least one memory server that stores contextual data. A router is configured to direct a recommendation request to a first stateless processor, which is configured to generate a recommendation using contextual data from the memory server. A controller monitors the available processing bandwidth of the stateless processors and deploys an additional stateless processor if the available processing bandwidth is less than a minimum available processing bandwidth threshold. The controller can remove from deployment a stateless processor if the available processing bandwidth is greater than a maximum pre-determined available processing bandwidth threshold.