Abstract: Systems and methods are provided that automatically process a message input, construct an intelligent query based on the processing of the message input, and provide at least one attachable entity according to the processing results and the intelligent query. In some example aspects, a message is received. A natural language processor to determine if the message is requesting content may then process the message. If the message is determined to be requesting content, then candidate sub-queries may be generated to serve as a training set for a query that will be sent to an external search engine to retrieve the attachable entity. The sub-queries may be ranked in order of relevance and performance score. The highest ranked sub-queries may then be used in the actual query that is fired against the external search engine. The external search engine may search local and remote repositories for the top K most relevant attachable entities and present them to a user for attachment in a reply message.

Abstract: Systems and methods are provided for generating training data from queries and user interactions associated with media collections related to the queries, and training a machine learning model using the generated training data to generate a trained machine learning model. The systems and methods further provide for receiving a prediction request comprising a query for relevant media collections, analyzing the query to determine query features, determining a plurality of media collections for the query, analyzing the plurality of media collections to determine media collection features for each media collection of the plurality of media collections, and generating, using the trained machine learning model, a semantic matching score for each media collection of the plurality of media collections based on matching the query features to the media collection features for each media collection of the plurality of media collections.

Abstract: Systems and methods are provided for generating training data from queries and user interactions associated with media collections related to the queries, and training a machine learning model using the generated training data to generate a trained machine learning model. The systems and methods further provide for receiving a prediction request comprising a query for relevant media collections, analyzing the query to determine query features, determining a plurality of media collections for the query, analyzing the plurality of media collections to determine media collection features for each media collection of the plurality of media collections, and generating, using the trained machine learning model, a semantic matching score for each media collection of the plurality of media collections based on matching the query features to the media collection features for each media collection of the plurality of media collections.

Abstract: Systems and methods are provided that automatically process a message input, construct an intelligent query based on the processing of the message input, and provide at least one attachable entity according to the processing results and the intelligent query. In some example aspects, a message is received. A natural language processor to determine if the message is requesting content may then process the message. If the message is determined to be requesting content, then candidate sub-queries may be generated to serve as a training set for a query that will be sent to an external search engine to retrieve the attachable entity. The sub-queries may be ranked in order of relevance and performance score. The highest ranked sub-queries may then be used in the actual query that is fired against the external search engine. The external search engine may search local and remote repositories for the top K most relevant attachable entities and present them to a user for attachment in a reply message.