Emotion-Aware Conversational Recommender Systems: a Case Study

TL;DR

This paper presents Gala, an emotion-aware conversational agent for online shopping that recognizes user emotions and adapts responses, enhancing user engagement and personalization in e-commerce.

Contribution

It introduces Gala, a novel emotion-aware virtual shopping assistant that integrates emotion recognition with personalized product recommendations using NLP and API integration.

Findings

Emotion-aware CAs improve shopping engagement.

Gala provides faster, more personalized shopping experiences.

User studies show positive reception of emotion-adaptive responses.

Abstract

In recent years, online shopping has grown rapidly, especially during the COVID-19 period. However, it still lacks elements typical of physical stores, such as empathic support and personalised advice from a sales assistant. This study explores how an emotion-aware Conversational Agent (CA) can improve the online shopping experience by responding to user emotions in a more natural and human way. The project focuses on Gala, a virtual assistant developed for the Galeries Lafayette website, capable of recognising emotional states from voice messages and adapting its responses accordingly. User needs were first analysed through semi-structured interviews, which informed the design of Gala's UX and functionalities. Gala was implemented using the OpenAI API and the Galeries Lafayette API, adopting a Content-Based recommendation approach. Through Natural Language Processing, it interprets user requests and retrieves products aligned with specific attributes such as name, price, and brand, enabling fluid dialogue and tailored suggestions. Two user studies were conducted: a usability test and a comparative evaluation between a standard CA and Gala's emotion-aware version. The results highlight the potential of emotion-aware CAs to make online shopping faster, more engaging, and closer to an in-store guided experience.