  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.
