Ranking expert responses and finding experts based on rank

摘要

In a computerized social network, expert and user chat sessions are stored and rated probabilistically. Later user requests for information are met with an expert ranking, based on a balance of similarities between expert profile and questions; similarity between expert profile and prior chat sessions, and dynamically updated chat session ratings. New sessions can be rated automatically with reference to keywords distilled from past sessions responsive to user ratings-and based on session length.