From Recommendation to Exploration: A Human-Centered Review of Conversational Recommender Systems
DOI:
https://doi.org/10.58445/rars.4238Keywords:
Conversational Recommender Systems, Natural language interaction, Human-centered CRS, Explainability, Exploration, User experienceAbstract
Conversational Recommender Systems (CRSs) have emerged as an important research area that combines recommendation algorithms with natural language interaction to provide more personalized and engaging user experiences. While current studies mostly focus on improving accuracy, dialogue management, and model performance, less attention has been given to user experience. This literature review examines the shift from system-centered research toward human-centered CRS design by synthesizing studies on explainability, trust, discoverability, preference elicitation, and exploratory information seeking. It compares different ways that help the user who is not sure what they are looking for to explore information instead of providing the most accurate recommendations. This review also discusses the current method for evaluating users’ experience and some improvements that could be made to it. Overall, this review argues that future research ought to not only be done towards improving recommendation accuracy but also on user experience by helping them to understand, explore, and make better decisions.
Keywords: Conversational Recommender Systems, Natural language interaction, Human-centered CRS, Explainability, Exploration, User experience
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