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Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM).
Language models are few-shot learners
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Matrix factorization techniques for recommender systems
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BPR: Bayesian Personalized Ranking from Implicit Feedback
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Imagenet classification with deep convolutional neural networks
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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Session-based recommendations with recurrent neural networks
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S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
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Wide & deep learning for recommender systems
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Session-based Recommendations with Recurrent Neural Networks
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Learning to generate product reviews from attributes
Dong, L.; Huang, S.; Wei, F.; Lapata, M.; Zhou, M.; and Xu, K. 2017 · 2017
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Neural rating regression with abstractive tips generation for recommendation
Li, P.; Wang, Z.; Ren, Z.; Bing, L.; and Lam, W. 2017 · 2017
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Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Attention is all you need
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Personalized top-n sequential recommendation via convolutional sequence embedding
Tang, J.; and Wang, K. 2018 · 2018
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Hierarchical gating networks for sequential recommendation
Ma, C.; Kang, P.; and Liu, X. 2019 · 2019
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Language models are unsupervised multitask learners
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BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
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Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
Yu, Z.; Lian, J.; Mahmoody, A.; Liu, G.; and Xie, X. 2019 · 2019
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Feature-level Deeper Self-Attention Network for Sequential Recommendation
Zhang, T.; Zhao, P.; Liu, Y.; Sheng, V. S.; Xu, J.; Wang, D.; Liu, G.; and Zhou, X. 2019 · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
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Glm: General language model pretraining with autoregressive blank infilling
Du, Z.; Qian, Y.; Liu, X.; Ding, M.; Qiu, J.; Yang, Z.; and Tang, J. 2021 · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning
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HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
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ReprBERT: Distilling BERT to an Efficient Representation-Based Relevance Model for E-Commerce
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Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Zhao, H.; Tan, H.; and Mei, H. 2022 · 2022
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Bao, K.; Zhang, J.; Zhang, Y.; Wang, W.; Feng, F.; and He, X. 2023 · 2023
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Personalized Transformer for Explainable Recommendation
Li, L.; Zhang, Y.; and Chen, L. 2021 · 2021
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A survey on representation learning for user modeling
Li, S.; and Zhao, H. 2021 · 2021
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SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Mao, K.; Zhu, J.; Wang, J.; Dai, Q.; Dong, Z.; Xiao, X.; and He, X. 2021 · 2021
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CTR-BERT: Cost-effective knowledge distillation for billion-parameter teacher models
Muhamed, A.; Keivanloo, I.; Perera, S.; Mracek, J.; Xu, Y.; Cui, Q.; Rajagopalan, S.; Zeng, B.; and Chilimbi, T. 2021 · 2021
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U-BERT: Pre-training user representations for improved recommendation
Qiu, Z.; Wu, X.; Gao, J.; and Fan, W. 2021 · 2021
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Knowledge-guided article embedding refinement for session-based news recommendation
Sheu, H.-S.; Chu, Z.; Qi, D.; and Li, S. 2021 · 2021
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Empowering news recommendation with pre-trained language models
Wu, C.; Wu, F.; Qi, T.; and Huang, Y. 2021 · 2021
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PALR: Personalization Aware LLMs for Recommendation
Chen, Z. 2023 · 2023
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Causal effect estimation: Recent advances, challenges, and opportunities
Chu, Z.; Huang, J.; Li, R.; Chu, W.; and Li, S. 2023 · 2023
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Uncovering ChatGPT’s Capabilities in Recommender Systems
Dai, S.; Shao, N.; Zhao, H.; Yu, W.; Si, Z.; Xu, C.; Sun, Z.; Zhang, X.; and Xu, J. 2023 · 2023
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Leveraging Large Language Models in Conversational Recommender Systems
Friedman, L.; Ahuja, S.; Allen, D.; Tan, T.; Sidahmed, H.; Long, C.; Xie, J.; Schubiner, G.; Patel, A.; Lara, H.; et al. 2023 · 2023
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Chat-rec: Towards interactive and explainable llms-augmented recommender system
Gao, Y.; Sheng, T.; Xiang, Y.; Xiong, Y.; Wang, H.; and Zhang, J. 2023 · 2023
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Robustness of Learning from Task Instructions
Gu, J.; Zhao, H.; Xu, H.; Nie, L.; Mei, H.; and Yin, W. 2023 · 2023
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Large language models are zero-shot rankers for recommender systems
Hou, Y.; Zhang, J.; Lin, Z.; Lu, H.; Xie, R.; McAuley, J.; and Zhao, W. X. 2023 · 2023
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Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Kang, W.-C.; Ni, J.; Mehta, N.; Sathiamoorthy, M.; Hong, L.; Chi, E.; and Cheng, D. Z. 2023 · 2023
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How Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J.; Dai, X.; Xi, Y.; Liu, W.; Chen, B.; Li, X.; Zhu, C.; Guo, H.; Yu, Y.; Tang, R.; et al. 2023 · 2023
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Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Shi, X.; Xue, S.; Wang, K.; Zhou, F.; Zhang, J. Y.; Zhou, J.; Tan, C.; and Mei, H. 2023 · 2023
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Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions
Tsai, C. F.; Zhou, X.; Liu, S. S.; Li, J.; Yu, M.; and Mei, H. 2023 · 2023
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Generative recommendation: Towards next-generation recommender paradigm
Wang, W.; Lin, X.; Feng, F.; He, X.; and Chua, T.-S. 2023 · 2023
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A Survey on Large Language Models for Recommendation
Wu, L.; Zheng, Z.; Qiu, Z.; Wang, H.; Gu, H.; Shen, T.; Qin, C.; Zhu, C.; Zhu, H.; Liu, Q.; et al. 2023 · 2023
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EasyTPP: Towards Open Benchmarking the Temporal Point Processes
Xue, S.; Shi, X.; Chu, Z.; Wang, Y.; Zhou, F.; Hao, H.; Jiang, C.; Pan, C.; Xu, Y.; Zhang, J. Y.; Wen, Q.; Zhou, J.; and Mei, H. 2023 · 2023
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Statler: State-Maintaining Language Models for Embodied Reasoning
Yoneda, T.; Fang, J.; Li, P.; Zhang, H.; Jiang, T.; Lin, S.; Picker, B.; Yunis, D.; Mei, H.; and Walter, M. R. 2023 · 2023
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Recommendation as instruction following: A large language model empowered recommendation approach
Zhang, J.; Xie, R.; Hou, Y.; Zhao, W. X.; Lin, L.; and Wen, J.-R. 2023 · 2023
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Explicit Planning Helps Language Models in Logical Reasoning
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