SNARC helps discovering content by highlighting whats meaningful in a quick, smart and personalized manner SNARC finds whats interesting by learning from the c

SNARC helps discovering content by highlighting whats meaningful in a quick, smart and personalized manner SNARC finds whats interesting by learning from the content, the social web and you. SNARC monitors and pre-qualifies URLs shared across my social network, then uses a three-fold process to filter the content that will be most useful to me: Content Modeling: SNARC parses the data from each piece of content, stripping out extraneous material and then analyzing the content for key terms, style, semantic classifications, and metadata. Community Modeling: The vetted documents are then relationship-correlated across the social web, building profiles for each user and each document that will be cross-referenced to match to your individual interests. User Modeling: SNARC watches both your passive and active feedback, what you do and don’t read on the app, as well as your thumbs up/thumbs down voting and checking of “more like this” boxes. Over time SNARC builds a model of your interests, and uses patterns in what you seem to like and dislike to more accurately filter the content it shows you. The result is a personalized search engine that actually learns what interests you to constantly show you better and better content.