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RecGPT: Generative Pre-training for Text-based Recommendation

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arxiv 2405.12715 v1 pith:2QYI3OHP submitted 2024-05-21 cs.IR cs.CL

classification cs.IRcs.CL
keywords recgptrecommendationtext-baseddatasetsmodelmodelspre-trainingrecgpt-7b-instruct
verification ladder T0 review T1 audit T2 compute T3 formal
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We present the first domain-adapted and fully-trained large language model, RecGPT-7B, and its instruction-following variant, RecGPT-7B-Instruct, for text-based recommendation. Experimental results on rating prediction and sequential recommendation tasks show that our model, RecGPT-7B-Instruct, outperforms previous strong baselines. We are releasing our RecGPT models as well as their pre-training and fine-tuning datasets to facilitate future research and downstream applications in text-based recommendation. Public "huggingface" links to our RecGPT models and datasets are available at: https://github.com/VinAIResearch/RecGPT

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  1. CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems

    cs.IR 2025-06 conditional novelty 6.0 of 10

    CoVE assigns each item a unique token ID, tunes item embeddings and the LM head, and predicts the next item from logits, beating finetune-and-retrieval baselines by up to 62 percent with a 16x compressed embedding table.

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