Abstract: Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing content recommendations to match the tastes and preferences of a group of users. a Recommendation Engine is used for generating two or more individual content recommendation sets for each of the members in the user group. A Recommendation Aggregation Module is used for adding and combining the individual content recommendation sets into an aggregated recommendation set. a Recommendation Selection Module is used for selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set. A Profile Engine is used for building individual group users profiles from which a merged group profile is constructed, or for building a single joint group profile based on inputs from multiple group users.

Abstract: Disclosed is a digital content targeting and delivery system. The system includes an interface to receive a primary content for distribution to one or more audience groups and a content feature detector to extract content features relevant to each of one or more audience groups. A relevant audience set generator parses a pool of potential audience member records into one or more (target) audience group lists by matching extracted content features with content preference parameters/fields of taste user profiles within the potential audience member records. a derivative content delivery module delivers, to members of at least one audience group, a derivative of the primary content including content segments with content features matching at least one common preference of the members of the at least one audience group.

Abstract: Disclosed are systems, methods, devices, circuits, and associated computer executable code for taste profiling of internet or network users. A User Events Analysis Server filters out vast amounts of irrelevant data, hard to isolate in conventional methods, and extracts valuable data from web-browsing or networking events. A User Taste Profiling Server automatically generates domain specific (e.g. media content) semantic taste profiles for users associated with the filtered and extracted web-browsing or networking events. Among other applications, such taste profiles may facilitate effective targeting of advertising campaigns in the given content domain.

Abstract: Disclosed are systems, apparatuses, circuits and methods for extrapolating meaning from vocalized speech or otherwise obtained text. Speech of a speaking user is sampled and digitized, the digitized speech is converted into a text stream, the text stream derived from speech or otherwise obtained is analyzed syntactically and semantically, a knowledgebase in the specific context domain of the text stream is utilized to construct one or more semantic/syntactic domain specific query analysis constrains/rule-sets, and a “Domain Specific Knowledgebase Query” (DSKQ) or set of queries is built at least partially based on the domain specific query analysis constrains/rule-sets.

Abstract: Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing hybrid content recommendations. One or more recommendation engines are collaboratively arranged based on the conditions of a recommendation request. The collaborative recommendation engine arrangement is used for generating a set of content recommendations for the request.

Abstract: Disclosed are systems, apparatuses, circuits, methods and computer executable code sets for generating and providing content recommendations to match the tastes and preferences of a group of users. a Recommendation Engine is used for generating two or more individual content recommendation sets for each of the members in the user group. A Recommendation Aggregation Module is used for adding and combining the individual content recommendation sets into an aggregated recommendation set. a Recommendation Selection Module is used for selecting at least a subset of the content items in the aggregated recommendation set for inclusion in a content recommendation result set. A Profile Engine is used for building individual group users profiles from which a merged group profile is constructed, or for building a single joint group profile based on inputs from multiple group users.