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The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations.
Language models are few-shot learners
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From zero-shot learning to cold-start recommendation
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Warm up cold-start advertisements: Improving ctr predictions via learning to learn id embeddings
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Multi-task recommendations with reinforcement learning
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Large language models are competitive near cold-start recommenders for language-and item-based preferences
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The dawn of lmms: Preliminary explorations with gpt-4v (ision)
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