Deep Learning for User Behaviour Prediction Using Streaming Analytics

Abstract

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings.