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Sequential recommender systems are essential for discerning user preferences from historical interactions and facilitating targeted recommendations.
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Llama 2: Open Foundation and Fine-Tuned Chat Models
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UDA: A user-difference attention for group recommendation
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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
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Towards Universal Sequence Representation Learning for Recommender Systems. In KDD ’22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 585–593
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RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation. In WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining . 571–581
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A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
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Large Language Models as Zero-Shot Conversational Recommenders. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 720–730
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LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
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Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
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CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation
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ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools
Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, and et al. 2024 · 2024
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Large Language Models are Zero-Shot Rankers for Recommender Systems. In Advances in Information Retrieval - 46th European Conference on Information Retrieval . 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian J. McAuley, and et al. 2024 · 2024
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ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation. In Proceedings of the ACM on Web Conference 2024 . 3497–3508
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LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
Xinyuan Wang, Liang Wu, Liangjie Hong, Hao Liu, and Yanjie Fu. 2024 · 2024
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CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Junda Wu, Cheng-Chun Chang, Tong Yu, Zhankui He, Jianing Wang, Yupeng Hou, and et al. 2024a · 2024
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NoteLLM: A Retrievable Large Language Model for Note Recommendation
Chao Zhang, Shiwei Wu, Haoxin Zhang, Tong Xu, Yan Gao, Yao Hu, and et al. 2024 · 2024
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Collaborative Large Language Model for Recommender Systems. In Proceedings of the ACM on Web Conference 2024 . 3162–3172
Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, and Jundong Li. 2024 · 2024
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