Follow-up question recommender for ai assistant

Abstract

One or more follow-up response recommendations relating to a question-answer event processed by an artificial intelligence assistant are generated. The one or more follow-up response recommendations identify questions from a predefined response library. A weighted average of embeddings is generated for each question-answer conversation of historical question-answer events collected via telemetry. A historical question-answer conversation includes a chronologically ordered sequence of question-answer events. A self-supervised weighted embedding dataset is generated and includes the weighted average of embeddings for each historical question-answer conversation and at least one corresponding semantic nearest neighbor question from the predefined response library. Topic weights of a follow-up question recommender are tuned by training the follow-up question recommender using the self-supervised weighted embedding dataset. The follow-up question recommender executes in an inference mode on the question-answer event to generate the one or more follow-up response recommendations of questions from the predefined response library.