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 filtering content from a data set includes accepting a search request directed to a data set associated with a site, the search request including a search term that is not among terms represented in a site-specific lookup table representing site-specific relatedness of terms in that data set, such related terms including any of synonyms, hypernyms and hyponyms; generating an approximating lookup table by applying a transformation function to a corpus lookup table, the corpus lookup table representing relatedness, in a general corpus, of terms in the data set; identifying terms represented in the approximating lookup table that are related terms of the search term; and filtering from the data set digital content that includes any of the search term and the terms identified from the approximating table as related terms of the search term.

Abstract: The foregoing are among the objects attained by the invention, which provides, in some aspects, digital data processing methods for generation of customized user interfaces that present links, images or other components representing items of interest to a user in an order that is prioritized as a function of (a) representations in a multidimensional factor space of characteristics of the respective items, and (b) representations in that same multidimensional space of characteristics of “context-indicative items”—e.g., items in which the user has previously shown an interest, as indicated by clicks or other interactions with those items respective components in the user interface.

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.

Abstract: A method of filtering content from a data set includes accepting a search request directed to a data set associated with a site, the search request including a search term that is not among terms represented in a site-specific lookup table representing site -specific relatedness of terms in that data set, such related terms including any of synonyms, hypernyms and hyponyms; generating an approximating lookup table by applying a transformation function to a corpus lookup table, the corpus lookup table representing relatedness, in a general corpus, of terms in the data set; identifying terms represented in the approximating lookup table that are related terms of the search term; and filtering from the data set digital content that includes any of the search term and the terms identified from the approximating table as related terms of the search term.

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: The invention provides in some aspects methods of digital data processor-based analysis of digital data that represent interactions to identify distinct individuals and/or the entities with which they are affiliated (e.g., households, businesses, social or other groups) involved in those interactions. The methods can be employed, for example, to analyze digital data representing retail purchase, marketing and visitor interactions for tracking and/or reporting purposes.

Abstract: The invention provides in some aspects methods of digital data processor-based analysis of digital data that represent interactions to identify distinct individuals and/or the entities with which they are affiliated (e.g., households, businesses, social or other groups) involved in those interactions. The methods can be employed, for example, to analyze digital data representing retail purchase, marketing and visitor interactions for tracking and/or reporting purposes.