Abstract: A generative recommender method and system applies trained neural networks to infer related concepts with respect to segments of temporally sequenced content that are inferred to be of particular interest to users. The inferred related concepts of interest may be embodied, for example, in the form vectorized embeddings of natural language and/or images. The embodied inferred related concepts of interest are then input into a generative process that applies trained neural networks to execute one or more vector embedding-based steps that result in generated content elements such as video that are based upon the related concepts of interest.

Abstract: A method and system for semantic-based navigation of temporally sequenced content such as videos interprets the image and audio-based content by applying computer-implemented neural networks and performs multi-modal inferences of temporally aligned content. The multi-modal inferences may be performed by means of the application of vectorized embeddings and/or by application of semantic chaining techniques. The multi-modal inferences are applied to generate navigational indicators and/or responses to user inputs that comprise natural language or images. The navigational indicators and responses to user inputs may be personalized based upon user behaviors.