  Interactive systems have taken over the web and mobile space with increasing
participation from users. Applications across every marketing domain can now be
accessed through mobile or web where users can directly perform certain actions
and reach a desired outcome. Actions of user on a system, though, can be
representative of a certain intent. Ability to learn this intent through user's
actions can help draw certain insight into the behavior of users on a system.
  In this paper, we present models to optimize interactive systems by learning
and analyzing user intent through their actions on the system. We present a
four phased model that uses time-series of interaction actions sequentially
using a Long Short-Term Memory (LSTM) based sequence learning system that helps
build a model for intent recognition. Our system then provides an objective
specific maximization followed by analysis and contrasting methods in order to
identify spaces of improvement in the interaction system. We discuss deployment
scenarios for such a system and present results from evaluation on an online
marketplace using user clickstream data.
