Sparse Intent Clustering Through Deep Context Encoders

A system for sparse intent clustering, the system comprising:

• a storage device configured to store program instructions;
• and
• one or more processors operably connected to the storage device and configured to execute the program instructions to cause the system to:
• identify features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description;
• encode the features of each user report into a binary vector;
• feed the binary vector for each user report into an autoencoder neural network, wherein the autoencoder neural network generates a N-dimensional float vector representing the user report;
• project the float vectors representing the user reports into a N-dimensional space;
• cluster the float vectors into vector clusters according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports;
• and
• label the intent of each vector cluster.

A computer program product for sparse intent clustering, the computer program product comprising:

• a computer-readable storage medium having program instructions embodied thereon to perform the steps of:
• identifying features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description;
• encoding the features of each user report into a binary vector;
• feeding the binary vector for each user report into an autoencoder neural network, wherein the autoencoder neural network generates a N-dimensional float vector representing the user report;
• projecting the float vectors representing the user reports into a N-dimensional space;
• clustering the float vectors into vector clusters according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports;
• and
• labeling the intent of each vector cluster.