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Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention.
Constrained k-means clustering
Paul S Bradley, Kristin P Bennett, and Ayhan Demiriz · 2000
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Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2012
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Kullback-leibler divergence constrained distributionally robust optimization
Zhaolin Hu and L Jeff Hong · 2013
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Hierarchical gating networks for sequential recommendation
Chen Ma, Peng Kang, and Xue Liu · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley · 2019
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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
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Autoregressive entity retrieval
N De Cao, G Izacard, S Riedel, and F Petroni · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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U-bert: Pre-training user representations for improved recommendation
Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan · 2021
Cited alongside, same era.
Language models as recommender systems: Evaluations and limitations
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang · 2021
Cited alongside, same era.
Introduction to algorithms
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2022
Cited alongside, same era.
M6-rec: Generative pretrained language models are open-ended recommender systems
Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang · 2022
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2022
Cited alongside, same era.
Autoregressive image generation using residual quantization
Cr-sorec: Bert driven consistency regularization for social recommendation
Tushar Prakash, Raksha Jalan, Brijraj Singh, and Naoyuki Onoe · 2023
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Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q Tran, Jonah Samost, et al · 2023
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On the theories behind hard negative sampling for recommendation
Wentao Shi, Jiawei Chen, Fuli Feng, Jizhi Zhang, Junkang Wu, Chongming Gao, and Xiangnan He · 2023
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Learning to tokenize for generative retrieval
Weiwei Sun, Lingyong Yan, Zheng Chen, Shuaiqiang Wang, Haichao Zhu, Pengjie Ren, Zhumin Chen, Dawei Yin, M. de Rijke, and Zhaochun Ren · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, and Wook-Shin Han · 2022
Cited alongside, same era.
Userbert: Pre-training user model with contrastive self-supervision
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang · 2022
Cited alongside, same era.
When auc meets dro: Optimizing partial auc for deep learning with non-convex convergence guarantee
Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, and Tianbao Yang · 2022
Cited alongside, same era.
Leveraging large language models for pre-trained recommender systems
Zhixuan Chu, Hongyan Hao, Xin Ouyang, Simeng Wang, Yan Wang, Yue Shen, Jinjie Gu, Qing Cui, Longfei Li, Siqiao Xue, et al · 2023
Cited alongside, same era.
Uncovering chatgpt’s capabilities in recommender systems
Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu · 2023
Cited alongside, same era.
An unified search and recommendation foundation model for cold-start scenario
Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, and Guannan Zhang · 2023
Cited alongside, same era.
How to index item ids for recommendation foundation models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang · 2023
Cited alongside, same era.
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Towards open-world recommendation with knowledge augmentation from large language models
Yunjia Xi, Weiwen Liu, Jianghao Lin, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu · 2023
Later among the works it cites.
Llara: Aligning large language models with sequential recommenders
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He · 2024
Closest in time.
Once: Boosting content-based recommendation with both open-and closed-source large language models
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu · 2024
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Towards llm-recsys alignment with textual id learning
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang · 2024
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Enhanced generative recommendation via content and collaboration integration
Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, et al · 2024
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Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang · 2024
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Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen · 2024
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