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As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.
Language models are few-shot learners
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Efficient estimation of word representations in vector space
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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Session-based recommendations with recurrent neural networks
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Self-attentive sequential recommendation
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Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
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Session-based recommendation with graph neural networks
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
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Feature-level deeper self-attention network for sequential recommendation
Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019 · 2019
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How can recommender systems benefit from large language models: A survey
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Llama: Open and efficient foundation language models
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