Abstract: Technologies described herein relate to dense retrieval and ranking of search results. A query indicating a computing context or user input is received. An embedding of the query is computed by way of a first encoder, and candidate results selected from a pool of potential results based upon the embedding of the query and embeddings of the potential results. A similarity score for a first of the candidate results is computed by way of a second encoder trained based upon an order metric that defines a ranking over a training set of potential results. The first encoder is trained based upon output of the second encoder prior to computing the embedding of the query. The candidate results are ranked based upon the similarity score of the first candidate result, and results responsive to the query are identified based upon the ranking. The identified results are output to a computing device.

Abstract: Technologies described herein relate to dense retrieval and ranking of search results. A query indicating a computing context or user input is received. An embedding of the query is computed by way of a first encoder, and candidate results selected from a pool of potential results based upon the embedding of the query and embeddings of the potential results. A similarity score for a first of the candidate results is computed by way of a second encoder trained based upon an order metric that defines a ranking over a training set of potential results. The first encoder is trained based upon output of the second encoder prior to computing the embedding of the query. The candidate results are ranked based upon the similarity score of the first candidate result, and results responsive to the query are identified based upon the ranking. The identified results are output to a computing device.

Abstract: The present disclosure discloses a method and an electronic device for recommending media data, the method includes: generating a regional feature vector of each region; receiving an instruction for obtaining recommended content; obtaining user information, historical access data and location information of a target user; forming an alternative media data group; scoring interest popularity of the target user on the media data in the alternative media data group; obtaining the regional feature vector related to the location information of the target user; performing regional information scoring on the media data in the alternative media data group; obtaining a comprehensive score of the media data in the alternative media data group by combining the interest popularity score of the target user with the regional information score; and recommending a plurality of media data with top ranked comprehensive scores to the target user.