Multi-objective optimization of explanation metrics in recommender systems with LLMs

Zanon, André Levi; da Rocha, Leonardo Chaves Dutra; Manzato, Marcelo Garcia

Multi-objective optimization of explanation metrics in recommender systems with LLMs

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Files

Multi-Objective_Optimization_of_Explanation_Metrics_in_Recommender_Systems_with_LLMs.pdf(602.37 KB)

Accepted Version

Date

2025-12-15

Authors

Zanon, André Levi

da Rocha, Leonardo Chaves Dutra

Manzato, Marcelo Garcia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published Version

10.1109/ICTAI66417.2025.00027

Abstract

Post-hoc Knowledge Graph (KG) explanation algorithms in Recommender Systems (RSs) identify the most relevant paths connecting interacted and recommended items based on shared attributes. These algorithms are categorized into syntactic and semantic approaches; however, neither can simultaneously optimize explanation quality metrics, which measure the recency of interacted items, diversity of attributes, and popularity of attributes shown across explanations. This study explores the use of Large Language Models (LLMs) to jointly optimize these metrics by presenting possible explanation paths for recommended items. The study evaluates three LLMs against both syntactic and semantic algorithms across two datasets and six RSs, finding that LLMs can improve explanation quality.

Keywords

Recommender Systems , Explainability , Recommendation explanation , Large Language Models

Citation

Zanon, A.L., Da Rocha, L.C.D. and Manzato, M.G. (2025) ‘Multi-objective optimization of explanation metrics in recommender systems with LLMs', 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, 03-05 November 2025, pp. 149–156. https://doi.org/10.1109/ICTAI66417.2025.00027