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This research aims to improve recommendation quality in specialized domains using knowledge integration with large language models.

Integrating Heterogeneous Knowledge for Enhanced Recommendation with Large Language Models

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This research aims to improve recommendation quality in specialized domains using knowledge integration with large language models.
Proposed REKALM framework integrates heterogeneous knowledge into LLM-based recommenders.
Utilized knowledge lexicalization to unify data sources into a natural language format.
Conducted experiments across four distinct domains to validate the framework.
Found that LLMs perform well in familiar domains, achieving satisfactory recommendations.
Demonstrated significant improvement in recommendation quality for specialized domains through knowledge integration.

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Randomized trial shows enhanced recommendations in specialized domains, indicating the need for knowledge integration.

Petruzzelli et al. (2026) studied this question.

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