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Recommender systems have been gaining increasing research attention over the years.
Item-based collaborative filtering recommendation algorithms. In WWW . 285–295
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
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Multi-sided Exposure Bias in Recommendation
Himan Abdollahpouri and Masoud Mansoury. 2020 · 2006
Earlier work this paper cites.
Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In SIGIR . 501–508
Jun Wang, Arjen P De Vries, and Marcel JT Reinders. 2006 · 2006
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In WWW . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
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Towards a More Realistic Evaluation: Testing the Ability to Predict Future Tastes of Matrix Factorization-Based Recommenders. In RecSys . 309–312
Pedro G. Campos, Fernando Díez, and Manuel Sánchez-Montañés. 2011 · 2011
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Exponential moving average versus moving exponential average
Frank Klinker. 2011 · 2011
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Managing Consumer Privacy Concerns in Personalization: A Strategic Analysis of Privacy Protection. In MIS Quarterly . 423–444
Dong-Joo Lee, Jae-Hyeon Ahn, and Youngsok Bang. 2011 · 2011
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Slim: Sparse linear methods for top-n recommender systems. In ICDM . 497–506
Xia Ning and George Karypis. 2011 · 2011
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The Application of Color Psychological Effect on Fashion Design. In Silk, Protective Clothing and Eco-Textiles , Vol. 796. 474–478
Jian Feng Zhang. 2013 · 2013
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Correcting Popularity Bias by Enhancing Recommendation Neutrality
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014 · 2014
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Haşim Sak, Andrew Senior, and Françoise Beaufays. 2014 · 2014
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Item Cold-Start Recommendations: Learning Local Collective Embeddings. In RecSys . 89–96
Martin Saveski and Amin Mantrach. 2014 · 2014
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Social Networks, Personalized Advertising, and Privacy Controls
Catherine E. Tucker. 2014 · 2014
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Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
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Herd Behavior
Tatsuya Kameda and Reid Hastie. 2015 · 2015
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Adam: A Method for Stochastic Optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Vista: A visually, socially, and temporally-aware model for artistic recommendation. In RecSys . 309–316
Ruining He, Chen Fang, Zhaowen Wang, and Julian McAuley. 2016 · 2016
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Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys . 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
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A dynamic recurrent model for next basket recommendation. In SIGIR . 729–732
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016 · 2016
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Controlling popularity bias in learning-to-rank recommendation. In RecSys . 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
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Sequential user-based recurrent neural network recommendations. In RecSys . 152–160
Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017 · 2017
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Neural attentive session-based recommendation. In CIKM . 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
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Personalizing session-based recommendations with hierarchical recurrent neural networks. In RecSys . 130–137
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017 · 2017
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What to Do Next: Modeling User Behaviors by Time-LSTM.. In IJCAI , Vol. 17. 3602–3608
Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017 · 2017
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Sequential Recommendation with User Memory Networks. In WSDM . 108–116
Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018 · 2018
Earlier work this paper cites.
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks. In SIGIR . 505–514
Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y. Chang. 2018 · 2018
Cited alongside, same era.
Self-attentive sequential recommendation. In ICDM . 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Cited alongside, same era.
An Adversarial Approach to Improve Long-Tail Performance in Neural Collaborative Filtering. In CIKM . 1491–1494
Adit Krishnan, Ashish Sharma, Aravind Sankar, and Hari Sundaram. 2018 · 2018
Cited alongside, same era.
STAMP: short-term attention/memory priority model for session-based recommendation. In SIGKDD . 1831–1839
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018 · 2018
Cited alongside, same era.
Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
How to Learn Item Representation for Cold-Start Multimedia Recommendation?. In MM . 3469–3477
Xiaoyu Du, Xiang Wang, Xiangnan He, Zechao Li, Jinhui Tang, and Tat-Seng Chua. 2020 · 2020
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In Praise of Herd Mentality
Robert H. Frank. 2020 · 2020
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Hierarchical User Profiling for E-Commerce Recommender Systems. In WSDM . 223–231
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, and Dawei Yin. 2020 · 2020
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A re-visit of the popularity baseline in recommender systems. In SIGIR . 1749–1752
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2020 · 2020
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Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour
Mary Loxton, Robert Truskett, Brigitte Scarf, Laura Sindone, George Baldry, and Yinong Zhao. 2020 · 2020
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Popularity Bias in Ranking and Recommendation. In AIES . 529–530
Himan Abdollahpouri. 2019 · 2019
Cited alongside, same era.
The unfairness of popularity bias in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 · 2019
Cited alongside, same era.
Privacy-preserving machine learning: Threats and solutions
Mohammad Al-Rubaie and J Morris Chang. 2019 · 2019
Cited alongside, same era.
Local popularity and time in top-n recommendation. In ECIR . 861–868
Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019 · 2019
Cited alongside, same era.
CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations. In RecSys . 228–236
Oren Barkan, Noam Koenigstein, Eylon Yogev, and Ori Katz. 2019 · 2019
Cited alongside, same era.
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. In RecSys
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019 · 2019
Cited alongside, same era.
Web Personalization Recommendation System Through Semantics. In ICCS . 658–661
Sukhmeen Kaur Hanjraw, Kuldeep Yadav, and Karamjit Kaur. 2019 · 2019
Cited alongside, same era.
Exploring data splitting strategies for the evaluation of recommendation models. In RecSys . 681–686
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020 · 2020
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Personalized graph neural networks with attention mechanism for session-aware recommendation
Mengqi Zhang, Shu Wu, Meng Gao, Xin Jiang, Ke Xu, and Liang Wang. 2020 · 2020
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The Effects of Herd Mentality on Behavior
Abdurhman Kurdi. 2021 · 2021
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Affective recommender systems in the educational field. A systematic literature review
Camilo Salazar, Jose Aguilar, Julián Monsalve-Pulido, and Edwin Montoya. 2021 · 2021
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Sparse-Interest Network for Sequential Recommendation. In WSDM . 598–606
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, and Xia Hu. 2021 · 2021
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Deconfounded recommendation for alleviating bias amplification. In SIGKDD . 1717–1725
Wenjie Wang, Fuli Feng, Xiangnan He, Xiang Wang, and Tat-Seng Chua. 2021 · 2021
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Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In SIGKDD . 1791–1800
Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021 · 2021
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Causal intervention for leveraging popularity bias in recommendation. In SIGIR . 11–20
Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021 · 2021
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Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li, and Shu Wu. 2021b · 2021
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Popularity-Opportunity Bias in Collaborative Filtering. In WSDM . 85–93
Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee. 2021 · 2021
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Intent contrastive learning for sequential recommendation. In WWW . 2172–2182
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022 · 2022
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AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang, Jae Boum Kim, Yingtao Luo, Kai Zhang, and Sunghun Kim. 2022 · 2022
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Data-driven Individual Influence Analysis: A Case Study of Chinese Film Industry. In IEIT . 173–177
Wenxuan Li, Yinghong Ma, and Lixin Zhao. 2022 · 2022
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Self-supervised hypergraph transformer for recommender systems. In SIGKDD . 2100–2109
Lianghao Xia, Chao Huang, and Chuxu Zhang. 2022 · 2022
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Neutralizing Popularity Bias in Recommendation Models. In SIGIR . 2623–2628
Guipeng Xv, Chen Lin, Hui Li, Jinsong Su, Weiyao Ye, and Yewang Chen. 2022 · 2022
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Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou, Xiangnan He, Xuezhi Cao, Fuzheng Zhang, and Wei Wu. 2022 · 2022
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Bias and debias in recommender system: A survey and future directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023 · 2023
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A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation
Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2023 · 2023
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Debiased recommendation with user feature balancing
Mengyue Yang, Guohao Cai, Furui Liu, Jiarui Jin, Zhenhua Dong, Xiuqiang He, Jianye Hao, Weiqi Shao, Jun Wang, and Xu Chen. 2023 · 2023
Closest in time.
Selfcf: A simple framework for self-supervised collaborative filtering
Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, and Chunyan Miao. 2023 · 2023
Closest in time.
Sequential recommendation via stochastic self-attention. In WWW . 2036–2047
Ziwei Fan, Zhiwei Liu, Yu Wang, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, and Philip S Yu. 2022 · 2047
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