Abstract: A system and method for question-based content answering that can include training a query-content model; indexing a collection of media content data forming indexed content; receiving a query input through a computer implemented computer interface; applying a retrieval model to the query input and indexed content and determining candidate content segment results, which may include: retrieving an initial set of candidate content segments by performing a keyword search of the query input on the indexed content, and ranking, based in part on language modeling using the query-content model, the initial set of candidate content segments into the candidate content segment results; and presenting the candidate content segment results in the computer interface.

Abstract: A system and method for a content-centric personalized recommendation engine that includes processing user data comprised of user feature data as input to a user neural network model and yielding a user embedding; processing the user embedding through a matchmaking neural network, which is a trained model to map user embeddings and content embeddings to a shared dimensional space, and yielding a user shared-item embedding; and applying analysis of the user shared-item embedding in selecting at least one content item associated with a content shared-item embedding within the matchmaking neural network.