A first user and a second user may exchange messages during a session and the exchanged messages may be processed to suggest resources to the first user. For example, the first user may be a customer service representative assisting a customer, and the suggested resources may include text of a message to send to the customer. The resources may be suggested by computing a semantic representation of the messages in the session, computing a context vector that describes a context of the session, computing a context hash vector from the context vector, and retrieving one or more resources from a data store of resources using the context hash vector. Information about a resource may be presented to the first user to assist the first user in responding to the second user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for presenting information about a resource to a user, the method performed by one or more server computers and comprising: receiving a plurality of electronic messages during a session between a first computing device of a first user and a second computing device of a second user; computing a message embedding for of message of the plurality of electronic messages with a first neural network, wherein each message embedding represents a corresponding message in a vector space; computing a first context vector by sequentially processing the message embeddings for the plurality of electronic messages, wherein the processing is performed using a second neural network; quantizing the first context vector to obtain a first context hash vector; selecting a first resource from a data store using the first context hash vector and a hash vector for the first resource, wherein (i) the data store comprises a plurality of resources, (ii) each resource of the plurality of resources is associated with a hash vector, (iii) selecting the first resource comprises computing a distance between the first context hash vector and the hash vector for the first resource; and transmitting, during the session, information about the first resource to the first computing device to allow the first user to access the first resource. 2. The computer-implemented method of claim 1, wherein the first user is a customer service representative and the second user is a customer. 3. The computer-implemented method of claim 1, wherein the method further comprises: receiving a subsequent message between the first user and the second user; computing a subsequent message embedding for the subsequent message; computing a second context vector using the first context vector and the subsequent message embedding of the subsequent message; quantizing the second context vector to obtain a second context hash vector; selecting a second resource from the data store using the second context hash vector and a hash vector for the second resource; and transmitting, during the session, information about the second resource to the first computing device. 4. The computer-implemented method of claim 1, wherein the second neural network is a recurrent neural network or a convolution neural network. 5. The computer-implemented method of claim 1, wherein each element of the first context hash vector comprises a boolean value. 6. The computer-implemented method of claim 1, wherein the first resource comprises text of a message, a document, an image, or a URL. 7. The computer-implemented method of claim 1, wherein the first context hash vector is equal to the hash vector for the first resource. 8. The computer-implemented method of claim 1, wherein the distance is a Hamming distance. 9. A system for presenting information about a resource to a user, the system comprising: at least one server computer comprising at least one processor and at least one memory, the at least one server computer configured to: receive, a plurality of electronic messages during a session between a first computing device of a first user and a second computing device of a second user; compute, a semantic representation of each message of the plurality of electronic messages; compute, a first context vector by processing the semantic representations for the plurality of electronic messages; quantize, the first context vector to obtain a first context hash vector; select a first resource from a data store using the first context hash vector and a hash vector for the first resource, wherein the data store comprises a plurality of resources and each resource of the plurality of resources is associated with a hash vector; and transmit, during the session, information about the first resource to the first computing device. 10. The system of claim 9, wherein the at least one server computer is configured to: receive a selection of the first resource by the first user; and cause the first resource to be transmitted to the second user. 11. The system of claim 9, wherein the semantic representation comprises a message embedding. 12. The system of claim 9, wherein the at least one server computer is configured to compute the first context vector using a recurrent neural network with long short-term memory units. 13. The system of claim 9, wherein the at least one server computer is configured to select the first resource using an inverted index. 14. The system of claim 9, wherein the at least one server computer is configured to quantize the first context vector by performing a rotation of the first context vector. 15. The system of claim 9, wherein the first context vector is computed using a neural network and the neural network is trained by minimizing a triplet rank loss function. 16. One or more non-transitory computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions comprising: receiving a plurality of electronic messages during a session between a first computing device of a first user and a second computing device of a second user; computing a semantic representation of each message of the plurality of electronic messages; computing a context vector by processing the semantic representations for the plurality of electronic messages; quantizing the context vector to obtain a context hash vector; selecting a first resource from a data store using the context hash vector and a hash vector for the first resource, wherein the data store comprises a plurality of resources and each resource of the plurality of resources is associated with a hash vector; and transmitting, during the session, information about the first resource to the first computing device. 17. The one or more non-transitory computer-readable media of claim 16, wherein each element of the context hash vector comprises a boolean value. 18. The one or more non-transitory computer-readable media of claim 16, wherein selecting the first resource from the data store comprises computing a Hamming distance between the context hash vector and the hash vector for the first resource. 19. The one or more non-transitory computer-readable media of claim 16, wherein the semantic representation comprises a message embedding. 20. The one or more non-transitory computer-readable media of claim 16, wherein selecting the first resource comprises using an inverted index.