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Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.
The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proc. of SIGIR . 335–336
Jaime Carbonell and Jade Goldstein. 1998 · 1998
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Novelty and diversity in information retrieval evaluation. In Proc. of SIGIR . 659–666
Charles LA Clarke, Maheedhar Kolla, Gordon V Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. 2008 · 2008
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
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A generic coordinate descent framework for learning from implicit feedback. In Proc. of WWW . 1341–1350
Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017 · 2017
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Neural collaborative filtering. In Proc. of WWW . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Considerations on recommendation independence for a find-good-items task
Toshihiro Kamishima and Shotaro Akaho. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Fa* ir: A fair top-k ranking algorithm. In Proc. of CIKM . 1569–1578
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Learning a deep listwise context model for ranking refinement. In Proc. of SIGIR . 135–144
Qingyao Ai, Keping Bi, Jiafeng Guo, and W Bruce Croft. 2018 · 2018
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Fast greedy map inference for determinantal point process to improve recommendation diversity
Laming Chen, Guoxin Zhang, and Eric Zhou. 2018 · 2018
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Fairness-aware tensor-based recommendation. In Proc. of CIKM . 1153–1162
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
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Personalized re-ranking for recommendation. In Proc. of RecSys . 3–11
Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, et al · 2019
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" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator
Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin. 2019 · 2019
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DTCDR: A framework for dual-target cross-domain recommendation. In Proc. of CIKM . 1533–1542
Feng Zhu, Chaochao Chen, Yan Wang, Guanfeng Liu, and Xiaolin Zheng. 2019 · 2019
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GPT-3: Its nature, scope, limits, and consequences
Luciano Floridi and Massimo Chiriatti. 2020 · 2020
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A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation. In Proc. of IJCAI . 3001–3008
Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, and Xiaolin Zheng. 2020 · 2020
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EMDKG: Improving Accuracy-Diversity Trade-Off in Recommendation with EM-based Model and Knowledge Graph Embedding. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology . 17–24
Lu Gan, Diana Nurbakova, Léa Laporte, and Sylvie Calabretto. 2021 · 2021
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An embedding learning framework for numerical features in ctr prediction. In Proc. of KDD . 2910–2918
Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, and Xiuqiang He. 2021 · 2021
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Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems. In Proc. of WWW . 1785–1797
Ruoxi Wang, Rakesh Shivanna, Derek Cheng, Sagar Jain, Dong Lin, Lichan Hong, and Ed Chi. 2021 · 2021
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Fairness-aware news recommendation with decomposed adversarial learning. In Proc. of AAAI . 4462–4469
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021 · 2021
Cited alongside, same era.
Policy-gradient training of fair and unbiased ranking functions. In Proc. of SIGIR . 1044–1053
Himank Yadav, Zhengxiao Du, and Thorsten Joachims. 2021 · 2021
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Diversification-aware learning to rank using distributed representation. In Proc. of WWW . 127–136
Le Yan, Zhen Qin, Rama Kumar Pasumarthi, Xuanhui Wang, and Michael Bendersky. 2021 · 2021
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KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos. In Proc. of CIKM . 3953–3957
Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, and Xiangnan He. 2022 · 2022
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Gromov-wasserstein guided representation learning for cross-domain recommendation. In Proc. of CIKM . 1199–1208
Xinhang Li, Zhaopeng Qiu, Xiangyu Zhao, Zihao Wang, Yong Zhang, Chunxiao Xing, and Xian Wu. 2022 · 2022
Mmmlp: Multi-modal multilayer perceptron for sequential recommendations. In Proc. of WWW . 1109–1117
Jiahao Liang, Xiangyu Zhao, Muyang Li, Zijian Zhang, Wanyu Wang, Haochen Liu, et al · 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, et al · 2023
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023b · 2023
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Yubo Ma, Yixin Cao, YongChing Hong, and Aixin Sun. 2023 · 2023
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Cited alongside, same era.
Attention over self-attention: Intention-aware re-ranking with dynamic transformer encoders for recommendation
Zhuoyi Lin, Sheng Zang, Rundong Wang, Zhu Sun, J Senthilnath, Chi Xu, and Chee Keong Kwoh. 2022 · 2022
Cited alongside, same era.
Neural re-ranking in multi-stage recommender systems: A review
Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, and Ruiming Tang. 2022b · 2022
Cited alongside, same era.
Fairness in rankings and recommendations: an overview
Evaggelia Pitoura, Kostas Stefanidis, and Georgia Koutrika. 2022 · 2022
Cited alongside, same era.
Measuring fairness in ranked results: An analytical and empirical comparison. In Proc. of SIGIR . 726–736
Amifa Raj and Michael D Ekstrand. 2022 · 2022
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Trustworthy recommender systems
Shoujin Wang, Xiuzhen Zhang, Yan Wang, and Francesco Ricci. 2022b · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al · 2023
Cited alongside, same era.
Recent advances in natural language processing via large pre-trained language models: A survey
Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, and Dan Roth. 2023 · 2023
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Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
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Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
Later among the works it cites.
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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Aligning large language models with human: A survey
Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2023b · 2023
Later among the works it cites.
Dq-lore: Dual queries with low rank approximation re-ranking for in-context learning
Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo, Yichun Yin, Enze Xie, Zhicheng Yang, Qingxing Cao, Haiming Wang, Xiongwei Han, et al · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
Later among the works it cites.
User retention-oriented recommendation with decision transformer. In Proc. of WWW . 1141–1149
Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, and Dawei Yin. 2023 · 2023
Later among the works it cites.
SMLP4Rec: An Efficient all-MLP Architecture for Sequential Recommendations
Jingtong Gao, Xiangyu Zhao, Muyang Li, Minghao Zhao, Runze Wu, Ruocheng Guo, Yiding Liu, and Dawei Yin. 2024 · 2024
Closest in time.
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion. In Proc. of NAACL . 2498–2518
Pengyue Jia, Yiding Liu, Xiangyu Zhao, Xiaopeng Li, Changying Hao, Shuaiqiang Wang, and Dawei Yin. 2024 · 2024
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Large Language Model Distilling Medication Recommendation Model
Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, and Yefeng Zheng. 2024c · 2024
Closest in time.
Large language models for generative information extraction: A survey
Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, and Enhong Chen. 2024a · 2024
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NoteLLM-2: Multimodal Large Representation Models for Recommendation
Chao Zhang, Haoxin Zhang, Shiwei Wu, Di Wu, Tong Xu, Yan Gao, Yao Hu, and Enhong Chen. 2024 · 2024
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Rating distribution calibration for selection bias mitigation in recommendations. In Proc. of WWW . 2048–2057
Haochen Liu, Da Tang, Ji Yang, Xiangyu Zhao, Hui Liu, Jiliang Tang, and Youlong Cheng. 2022a · 2057
Closest in time.