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Sequential Recommenders generate recommendations based on users' historical interaction sequences.
Language models are few-shot learners
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Learning to denoise unreliable interactions for graph collaborative filtering. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval . 122–132
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Large language models are zero-shot rankers for recommender systems. In European Conference on Information Retrieval . Springer, 364–381
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Learning vector-quantized item representation for transferable sequential recommenders. In Proceedings of the ACM Web Conference 2023 . 1162–1171
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Retrieving supporting evidence for generative question answering. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region . 11–20
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Survey of hallucination in natural language generation
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Exploring fine-tuning chatgpt for news recommendation
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A self-correcting sequential recommender. In Proceedings of the ACM Web Conference 2023 . 1283–1293
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Demystifying embedding spaces using large language models
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D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
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Representation learning with large language models for recommendation. In Proceedings of the ACM on Web Conference 2024 . 3464–3475
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Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning
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Large Language Model Enhanced Hard Sample Identification for Denoising Recommendation
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Can Small Language Models be Good Reasoners for Sequential Recommendation?. In Proceedings of the ACM on Web Conference 2024 . 3876–3887
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Tired of Plugins? Large Language Models Can Be End-To-End Recommenders
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Soft Contrastive Sequential Recommendation
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