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The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues.
Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation
Heung-Nam Kim, Ae-Ttie Ji, Inay Ha, and Geun-Sik Jo. 2010 · 2010
Earlier work this paper cites.
Challenging the long tail recommendation
Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012 · 2012
Earlier work this paper cites.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Earlier work this paper cites.
Stochastic processes and applications
Grigorios A Pavliotis. 2016 · 2016
Earlier work this paper cites.
Recommendations as treatments: Debiasing learning and evaluation. In international conference on machine learning . PMLR, 1670–1679
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016 · 2016
Earlier work this paper cites.
Learning to rank with selection bias in personal search. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 115–124
Xuanhui Wang, Michael Bendersky, Donald Metzler, and Marc Najork. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Earlier work this paper cites.
Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Causal embeddings for recommendation. In Proceedings of the 12th ACM conference on recommender systems . 104–112
Stephen Bonner and Flavian Vasile. 2018 · 2018
Earlier work this paper cites.
Recommending long-tail items using extended tripartite graphs. In 2018 IEEE International Conference on Big Knowledge (ICBK) . IEEE, 123–130
Andrew Luke, Joseph Johnson, and Yiu-Kai Ng. 2018 · 2018
Earlier work this paper cites.
What is the effect of importance weighting in deep learning?. In International conference on machine learning . PMLR, 872–881
Jonathon Byrd and Zachary Lipton. 2019 · 2019
Earlier work this paper cites.
Class-balanced loss based on effective number of samples. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 9268–9277
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie. 2019 · 2019
Earlier work this paper cites.
Improving generative visual dialog by answering diverse questions
Vishvak Murahari, Prithvijit Chattopadhyay, Dhruv Batra, Devi Parikh, and Abhishek Das. 2019 · 2019
Earlier work this paper cites.
Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 188–197
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Earlier work this paper cites.
Sampling-bias-corrected neural modeling for large corpus item recommendations. In Proceedings of the 13th ACM Conference on Recommender Systems . 269–277
Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019 · 2019
Earlier work this paper cites.
Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 34. 19–26
Chong Chen, Min Zhang, Yongfeng Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma. 2020 · 2020
Earlier work this paper cites.
Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2493–2500
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, and Dawei Yin. 2020 · 2020
Earlier work this paper cites.
Modeling and counteracting exposure bias in recommender systems
Sami Khenissi and Olfa Nasraoui. 2020 · 2020
Earlier work this paper cites.
Long-tail session-based recommendation. In Proceedings of the 14th ACM Conference on Recommender Systems . 509–514
Siyi Liu and Yujia Zheng. 2020 · 2020
Cited alongside, same era.
Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar. 2020 · 2020
Cited alongside, same era.
Correcting for selection bias in learning-to-rank systems. In Proceedings of The Web Conference 2020 . 1863–1873
Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. 2020 · 2020
Cited alongside, same era.
Mitigating long tail effect in recommendations using few shot learning technique
Rama Syamala Sreepada and Bidyut Kr Patra. 2020 · 2020
Cited alongside, same era.
User-centered evaluation of popularity bias in recommender systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization . 119–129
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
Later among the works it cites.
FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4. In Findings of the Association for Computational Linguistics: EMNLP 2023 . 3763–3775
Jeonghwan Kim, Giwon Hong, Sung-Hyon Myaeng, and Joyce Whang. 2023 · 2023
Later among the works it cites.
Theory of mind for multi-agent collaboration via large language models
Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, and Katia Sycara. 2023a · 2023
Later among the works it cites.
Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems . 820–825
Yaokun Liu, Xiaowang Zhang, Minghui Zou, and Zhiyong Feng. 2023 · 2023
Later among the works it cites.
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Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, and Edward Malthouse. 2021 · 2021
Cited alongside, same era.
Reenvisioning the comparison between neural collaborative filtering and matrix factorization. In Proceedings of the 15th ACM Conference on Recommender Systems . 521–529
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, and Claudio Pomo. 2021 · 2021
Cited alongside, same era.
Correcting exposure bias for link recommendation. In International Conference on Machine Learning . PMLR, 3953–3963
Shantanu Gupta, Hao Wang, Zachary Lipton, and Yuyang Wang. 2021 · 2021
Cited alongside, same era.
Leave no user behind: Towards improving the utility of recommender systems for non-mainstream users. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 103–111
Roger Zhe Li, Julián Urbano, and Alan Hanjalic. 2021 · 2021
Cited alongside, same era.
Stable-baselines3: Reliable reinforcement learning implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. 2021 · 2021
Cited alongside, same era.
CADPP: An Effective Approach to Recommend Attentive and Diverse Long-tail Items. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology . 218–225
Shuai Tang and Xiaofeng Zhang. 2021 · 2021
Cited alongside, same era.
Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1791–1800
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021 · 2021
Cited alongside, same era.
Deconfounded and explainable interactive vision-language retrieval of complex scenes. In Proceedings of the 29th ACM International Conference on Multimedia . 2103–2111
Junda Wu, Tong Yu, and Shuai Li. 2021 · 2021
Cited alongside, same era.
Improving Long-Tail Item Recommendation with Graph Augmentation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management . 1707–1716
Sichun Luo, Chen Ma, Yuanzhang Xiao, and Linqi Song. 2023 · 2023
Later among the works it cites.
Large Language Models are Not Stable Recommender Systems
Tianhui Ma, Yuan Cheng, Hengshu Zhu, and Hui Xiong. 2023 · 2023
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Lkpnr: Llm and kg for personalized news recommendation framework
Xie Runfeng, Cui Xiangyang, Yan Zhou, Wang Xin, Xuan Zhanwei, Zhang Kai, et al · 2023
Later among the works it cites.
Large language models are competitive near cold-start recommenders for language-and item-based preferences. In Proceedings of the 17th ACM conference on recommender systems . 890–896
Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, and Lucas Dixon. 2023 · 2023
Later among the works it cites.
Can ChatGPT Replace Traditional KBQA Models? An In-Depth Analysis of the Question Answering Performance of the GPT LLM Family. In International Semantic Web Conference . Springer, 348–367
Yiming Tan, Dehai Min, Yu Li, Wenbo Li, Nan Hu, Yongrui Chen, and Guilin Qi. 2023 · 2023
Later among the works it cites.
Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
Jianing Wang, Qiushi Sun, Nuo Chen, Xiang Li, and Ming Gao. 2023c · 2023
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Wenjie Wang, Yiyan Xu, Fuli Feng, Xinyu Lin, Xiangnan He, and Tat-Seng Chua. 2023d · 2023
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Recmind: Large language model powered agent for recommendation
Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang. 2023a · 2023
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DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
Yu Wang, Zhiwei Liu, Jianguo Zhang, Weiran Yao, Shelby Heinecke, and Philip S Yu. 2023b · 2023
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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. 2023 · 2023
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User-regulation deconfounded conversational recommender system with bandit feedback. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2694–2704
Yu Xia, Junda Wu, Tong Yu, Sungchul Kim, Ryan A Rossi, and Shuai Li. 2023 · 2023
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Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
Jing Yao, Wei Xu, Jianxun Lian, Xiting Wang, Xiaoyuan Yi, and Xing Xie. 2023 · 2023
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Thought propagation: An analogical approach to complex reasoning with large language models
Junchi Yu, Ran He, and Rex Ying. 2023 · 2023
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A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1760–1764
Fan Zhang and Qijie Shen. 2023 · 2023
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Robust Recommender System: A Survey and Future Directions
Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, Huawei Shen, and Xueqi Cheng. 2023a · 2023
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Wenxuan Zhang, Hongzhi Liu, Yingpeng Du, Chen Zhu, Yang Song, Hengshu Zhu, and Zhonghai Wu. 2023c · 2023
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Collm: Integrating collaborative embeddings into large language models for recommendation
Yang Zhang, Fuli Feng, Jizhi Zhang, Keqin Bao, Qifan Wang, and Xiangnan He. 2023b · 2023
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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. 2023 · 2023
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Interact with the Explanations: Causal Debiased Explainable Recommendation System. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining . 472–481
Xu Liu, Tong Yu, Kaige Xie, Junda Wu, and Shuai Li. 2024 · 2024
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Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu, Ming Gao, and Julian McAuley. 2024 · 2024
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