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

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dc.contributor.authorZanon, André Levien
dc.contributor.authorda Rocha, Leonardo Chaves Dutraen
dc.contributor.authorManzato, Marcelo Garciaen
dc.date.accessioned2026-01-06T16:31:29Z
dc.date.available2026-01-06T16:31:29Z
dc.date.issued2025-12-15en
dc.description.abstractPost-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.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationZanon, 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/ICTAI66417.2025.00027en
dc.identifier.doi10.1109/ICTAI66417.2025.00027en
dc.identifier.eissn2375-0197en
dc.identifier.endpage156en
dc.identifier.issn1082-3409en
dc.identifier.startpage149en
dc.identifier.urihttps://hdl.handle.net/10468/18370
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urihttps://ieeexplore.ieee.org/xpl/conhome/11272175/proceedingen
dc.rights© 2025, IEEE. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.statusNot peer revieweden
dc.subjectRecommender Systemsen
dc.subjectExplainabilityen
dc.subjectRecommendation explanationen
dc.subjectLarge Language Modelsen
dc.titleMulti-objective optimization of explanation metrics in recommender systems with LLMsen
dc.typeConference itemen
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