A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
Adaptive Candidate Retrieval with Dynamic Knowledge Graph Construction for Cold-Start Recommendation
4 Pith papers cite this work. Polarity classification is still indexing.
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MixRAGRec is a multi-agent KG-RAG framework with an MoE retrieval agent for query-specific granularity, a knowledge alignment agent, and a contrastive recommendation agent trained jointly via MMAPO.
LLM rerankers in cold-start recsys show recall@200 of 0.109, concentrate on only 3 items, and are beaten by popularity baselines (HR@10 0.268 vs 0.008).
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.
citing papers explorer
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Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
A systematic review of over 200 studies concludes that LLMs in recommender systems act as a double-edged sword, creating both opportunities and new risks for trustworthiness.
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Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation
MixRAGRec is a multi-agent KG-RAG framework with an MoE retrieval agent for query-specific granularity, a knowledge alignment agent, and a contrastive recommendation agent trained jointly via MMAPO.
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Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations
LLM rerankers in cold-start recsys show recall@200 of 0.109, concentrate on only 3 items, and are beaten by popularity baselines (HR@10 0.268 vs 0.008).
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Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback
Advocates prioritizing explicit contextual feedback in LLM-based recommender systems to improve user preference alignment and explainability.