1. A system that uses conversational similarity to reduce dimensional complexity within contact center analysis operations, comprising: a self-learning interaction optimizer comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing, wherein the programmable instructions, when operating on the processor, cause the processor to: observe and analyze string-based data from a contact center system via a network; produce a plurality of information similarity vectors based at least in part on the analysis; provide at least a portion of the information similarity vectors as input variables to a dilated convolutional artificial neural network; receive a plurality of output vectors from the dilated convolutional artificial neural network; produce context data based at least in part on the output vectors; associate at least a portion of the context data with at least a portion of the string-based data; wherein the self-learning interaction optimizer transmits at least a portion of the string-based data and the corresponding associated context data as output to a plurality of contact center resources, the contact center resources comprising a routing server; and wherein the routing server routes an inbound interaction to a destination contact center resource based at least in part on the received context data associated with string-based data.