Cross-Domain Recommendations

Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations
Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations

This paper presents a strategy for explainable cross-domain recommendations (CDR) using large language models (LLMs). CDR is challenging due to data sparsity, as it requires extensive labeled data across both source and target domains, which is hard to collect. Our approach leverages the knowledge in LLMs to bridge these domains and provide personalized recommendations.

Oct 8, 2024