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The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models.
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 1902
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Extensions of Lipshitz mapping into Hilbert space. In
William B Johnson. 1984 · 1984
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Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting
Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, and Xiangzhan Yu. 2020 · 2004
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An MDP-Based Recommender System
Guy Shani, David Heckerman, and Ronen I. Brafman. 2005 · 2005
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The Probabilistic Relevance Framework: BM25 and Beyond
Stephen Robertson and Hugo Zaragoza. 2009 · 2009
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Beyond Accuracy: Evaluating Recommender Systems by Coverage and Serendipity. In
Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010 · 2010
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Solving the apparent diversity-accuracy dilemma of recommender systems
Tao Zhou, Zoltán Kuscsik, Jian-Guo Liu, Matúš Medo, Joseph Rushton Wakeling, and Yi-Cheng Zhang. 2010 · 2010
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Algorithms for Hyper-Parameter Optimization. In
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011 · 2011
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Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In
Saúl Vargas and Pablo Castells. 2011 · 2011
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Playlist prediction via metric embedding. In
Shuo Chen, Josh L. Moore, Douglas Turnbull, and Thorsten Joachims. 2012 · 2012
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Personalized News Recommendation with Context Trees. In
Florent Garcin, Christos Dimitrakakis, and Boi Faltings. 2013 · 2013
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Min Lin, Qiang Chen, and Shuicheng Yan. 2014 · 2014
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What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015b · 2015
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Personalized tour recommendation based on user interests and points of interest visit durations. In
Kwan Hui Lim, Jeffrey Chan, Christopher Leckie, and Shanika Karunasekera. 2015 · 2015
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Deep Neural Networks for YouTube Recommendations. In
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In
Ruining He and Julian McAuley. 2016 · 2016
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Session-based Recommendations with Recurrent Neural Networks. In
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
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When Recurrent Neural Networks Meet the Neighborhood for Session-Based Recommendation. In
Dietmar Jannach and Malte Ludewig. 2017 · 2017
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Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017 · 2017
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Attention is All you Need. In
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Self-Attentive Sequential Recommendation. In
Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018
Earlier work this paper cites.
Evaluation of Session-based Recommendation Algorithms
Malte Ludewig and Dietmar Jannach. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Neural News Recommendation with Long- and Short-term User Representations. In
Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, and Xing Xie. 2019 · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Neural News Recommendation with Multi-Head Self-Attention. In
Chuhan Wu, Fangzhao Wu, Suyu Ge, Tao Qi, Yongfeng Huang, and Xing Xie. 2019b · 2019
Cited alongside, same era.
Language Models are Few-Shot Learners. In
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
Research directions in session-based and sequential recommendation
Dietmar Jannach, Bamshad Mobasher, and Shlomo Berkovsky. 2020 · 2020
Uncovering ChatGPT’s Capabilities in Recommender Systems. In
Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023
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Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models’ Reasoning Performance
Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, and Tushar Khot. 2023 · 2023
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Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
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Leveraging Large Language Models for Sequential Recommendation. In
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
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TabLLM: Few-shot Classification of Tabular Data with Large Language Models. In
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. 2023 · 2023
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Cited alongside, same era.
Evaluating content novelty in recommender systems
Marcelo Mendoza and Nicolás Torres. 2020 · 2020
Cited alongside, same era.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets. In
Patrick Lewis, Pontus Stenetorp, and Sebastian Riedel. 2021 · 2021
Cited alongside, same era.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search. In
Yiding Liu, Weixue Lu, Suqi Cheng, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, and Dawei Yin. 2021 · 2021
Cited alongside, same era.
Empirical Analysis of Session-Based Recommendation Algorithms
Malte Ludewig, Sara Latifi, Noemi Mauro, and Dietmar Jannach. 2021 · 2021
Cited alongside, same era.
Empowering News Recommendation with Pre-Trained Language Models. In
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2021 · 2021
Cited alongside, same era.
Language models as recommender systems: Evaluations and limitations. In
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021a · 2021
Cited alongside, same era.
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Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders. In
Yupeng Hou, Zhankui He, Julian McAuley, and Wayne Xin Zhao. 2023a · 2023
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How to Index Item IDs for Recommendation Foundation Models. In
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
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Large Language Models Struggle to Learn Long-Tail Knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
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BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model
Zhiyu Li, Yanfang Chen, Xuan Zhang, and Xun Liang. 2023a · 2023
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How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, and Weinan Zhang. 2023 · 2023
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An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Yun Luo, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2023 · 2023
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UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation. In
Zhiming Mao, Huimin Wang, Yiming Du, and Kam-Fai Wong. 2023 · 2023
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Orca 2: Teaching Small Language Models How to Reason
Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andres Codas, Clarisse Simoes, Sahaj Agarwal, Xuxi Chen, Anastasia Razdaibiedina, Erik Jones, Kriti Aggarwal, Hamid Palangi, Guoqing Zheng, Corby Rosset, Hamed Khanpour, and Ahmed Awadallah. 2023 · 2023
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Generative Sequential Recommendation with GPTRec
Aleksandr V. Petrov and Craig Macdonald. 2023 · 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, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, and Yue Zhang. 2023 · 2023
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Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Lei Wang and Ee-Peng Lim. 2023 · 2023
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A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2023 · 2023
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PALR: Personalization Aware LLMs for Recommendation
Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. 2023 · 2023
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Where to Go Next for Recommender Systems? ID- vs. Modality-Based Recommender Models Revisited. In
Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023 · 2023
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Recommendation as instruction following: A large language model empowered recommendation approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023 · 2023
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Prompt Learning for News Recommendation
Zizhuo Zhang and Bang Wang. 2023 · 2023
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Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
Oded Ovadia, Menachem Brief, Moshik Mishaeli, and Oren Elisha. 2024 · 2024
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Scaling Law of Large Sequential Recommendation Models. In
Gaowei Zhang, Yupeng Hou, Hongyu Lu, Yu Chen, Wayne Xin Zhao, and Ji-Rong Wen. 2024 · 2024
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