Intent Recognition in Conversational Recommender Systems

TL;DR

This paper reviews recent advances in intent recognition within conversational recommender systems, proposing a pipeline that leverages transformer models and reverse feature engineering to improve understanding of customer queries in chatbots.

Contribution

It introduces a novel pipeline for contextualizing utterances and linking them to ML models for better intent recognition in chatbot-based CRS.

Findings

Transformer models outperform traditional methods in intent recognition.

The proposed pipeline improves understanding of customer queries.

Evaluation on MSDialogue dataset demonstrates effectiveness.

Abstract

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.