Learning User Intent from Action Sequences on Interactive Systems

TL;DR

This paper introduces an LSTM-based model to analyze user action sequences on interactive systems, aiming to accurately infer user intent and optimize system performance through sequential learning and analysis.

Contribution

It presents a novel four-phase LSTM-based framework for intent recognition from user actions, enabling system optimization and insight extraction.

Findings

Effective intent recognition from clickstream data

Improved system performance through targeted analysis

Potential for deployment in various interactive platforms

Abstract

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.