Symbiotic Conversational Recommender Systems: A New Approach to Improving Transparency and Persuasion

This project proposal explores the concept of human-machine symbiosis within conversational recommender systems (CRSs), aiming to develop CRSs that are more transparent, and persuasive and can better support users in decision-making tasks. One way to equip CRSs with these capabilities is by making use of the potential of large language models (LLMs). The research focuses on three main research directions: (1) integrating knowledge into LLMs to optimize their recommendation capabilities in CRSs; (2) exploring new ways to fine-tune a pre-trained model for conversational recommendations, without forgetting the knowledge it has already learned; (3) evaluating the impact of an LLM-based CRS on users in terms of transparency, engagement, and persuasion. Through this research, the aim is to overcome limitations in current CRS approaches and develop a more collaborative and user-centric recommendation system.