Abstract: We have developed a system that automatically suggests macros to help customer-support agents process customer-support tickets in an online customer-support system. During operation, the system receives a customer-support ticket, which is associated with a request from a customer in the customer-support system, wherein the request relates to a product or a service used by the customer. Next, the system converts text from the customer-support ticket into a ticket embedding in a vector space. The system then feeds the ticket embedding into a macro-suggestion model, which correlates ticket embeddings with macros, wherein each of the macros comprises a sequence of commands that performs an operation to facilitate processing of the customer-support ticket. If the macro-suggestion model produces suggested macros, the system presents the suggested macros to a customer-support agent.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.