Author ORCID Identifier

Tsai - https://orcid.org/0000-0001-9188-0362

Document Type

Conference Proceeding

Publication Date

8-13-2024

Publication Title

International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence

First Page

73

Last Page

84

DOI

https://doi-org.leo.lib.unomaha.edu/10.1007/978-3-031-66635-3_7

Abstract

This paper introduces an innovative approach to recommender systems through the development of an explainable architecture that leverages large language models (LLMs) and prompt engineering to provide natural language explanations. Traditional recommender systems often fall short in offering personalized, transparent explanations, particularly for users with varying levels of digital literacy. Focusing on the Advisor Recommender System, our proposed system integrates the conversational capabilities of modern AI to deliver clear, context-aware explanations for its recommendations. This research addresses key questions regarding the incorporation of LLMs into social recommender systems, the impact of natural language explanations on user perception, and the specific informational needs users prioritize in such interactions. A pilot study with 11 participants reveals insights into the system’s usability and the effectiveness of explanation clarity. Our study contributes to the broader human-AI interaction literature by outlining a novel system architecture, identifying user interaction patterns, and suggesting directions for future enhancements to improve decision-making processes in AI-driven recommendations.

Comments

This version of the Chapter has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi-org.leo.lib.unomaha.edu/10.1007/978-3-031-66635-3_7

Publisher holds a Bespoken License

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