RSLLM mixes item ID embeddings from classical recommenders with text titles inside an LLM prompt and uses two-stage contrastive fine-tuning to improve sequential recommendation.
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Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models
RSLLM mixes item ID embeddings from classical recommenders with text titles inside an LLM prompt and uses two-stage contrastive fine-tuning to improve sequential recommendation.