Developing Smart Conversation Agent ECOM-BOT for Ecommerce Applications using Deep Learning and Pattern Matching

Abstract

Chatbots are a technological leap in conversational services, generating messages to users either following a set of rules to respond based on recognized patterns or training themselves from previous data or conversations. The primary goal is to enable a device to communicate with a user upon receiving natural language user requests using artificial intelligence and machine learning to generate automated responses. Technology is progressively catering to the questions, both in academic and business contexts, such as situations that require agents to investigate the cause of customer dissatisfaction or to recommend products and services. Significance of this research is to reduce the human dependency and improving customer support by providing close to human natural responses using pattern matching and deep learning on the custom-made data. The main objective of this work is to (a) study the existing literature on cutting-edge technologies in chatbot development in terms of research trends, legacy components, techniques, datasets, and domains specifically in e-commerce and (b) to develop a product that fill some of the gaps/missing functionality identified in current frameworks. We have achieved the following, (a) generated small yet generic dataset, which can be used for all types of products, (b) the intents are identified accurately by the bot using deep learning, whenever a user query.