LLM2Rec combines next-item prediction fine-tuning with masked token reconstruction and contrastive learning to produce item embeddings that outperform existing text-embedding baselines for sequential recommendation.
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LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
LLM2Rec combines next-item prediction fine-tuning with masked token reconstruction and contrastive learning to produce item embeddings that outperform existing text-embedding baselines for sequential recommendation.