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Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue.
Using collaborative filtering to weave an information tapestry
David Goldberg, David Nichols, Brian M Oki, and Douglas Terry. 1992 · 1992
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FP-outlier: frequent pattern based outlier detection
Zengyou He, Xiaofei Xu, Zhexue Joshua Huang, and Shengchun Deng. 2005 · 2005
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Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In
Shyong K Lam, Dan Frankowski, and et al. 2006 · 2006
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Trust and risk evaluation of transactions with different amounts in peer-to-peer e-commerce environments. In
Yan Wang and Fu-ren Lin. 2006 · 2006
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Trust-aware recommender systems. In
Paolo Massa and Paolo Avesani. 2007 · 2007
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Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness
Bamshad Mobasher, Robin Burke, Runa Bhaumik, and et al. 2007 · 2007
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Visualizing data using t-SNE
Laurens Van Maaten and et al. 2008 · 2008
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Evaluating transaction trust and risk levels in peer-to-peer e-commerce environments
Yan Wang, Duncan S Wong, and et al. 2008 · 2008
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Improving aggregate recommendation diversity using ranking-based techniques
Gediminas Adomavicius and YoungOk Kwon. 2011 · 2011
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HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In
Zhiang Wu and et al. 2012 · 2012
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A trust vector approach to transaction context-aware trust evaluation in e-commerce and e-service environments. In
Haibin Zhang, Yan Wang, and Xiuzhen Zhang. 2012 · 2012
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Speech enhancement based on deep denoising autoencoder. In
Xugang Lu, Yu Tsao, Shigeki Matsuda, and Chiori Hori. 2013 · 2013
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Commtrust: computing multi-dimensional trust by mining e-commerce feedback comments
Xiuzhen Zhang, Lishan Cui, and Yan Wang. 2013 · 2013
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Shilling attacks against recommender systems: a comprehensive survey
Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014 · 2014
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Word2vec parameter learning explained
Xin Rong. 2014 · 2014
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Trust prediction with propagation and similarity regularization. In
Xiaoming Zheng, Yan Wang, Mehmet Orgun, and et al. 2014 · 2014
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Novelty and diversity in recommender systems
Pablo Castells, Neil J. Hurley, and Saul Vargas. 2015 · 2015
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Evaluating Recommender Systems
Asela Gunawardana and Guy Shani. 2015 · 2015
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A comprehensive survey of neighborhood-based recommendation methods
Xia Ning, Christian Desrosiers, and et al. 2015 · 2015
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Social context-aware trust inference for trust enhancement in social network based recommendations on service providers
Yan Wang, Lei Li, and Guanfeng Liu. 2015 · 2015
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Reputationpro: the efficient approaches to contextual transaction trust computation in e-commerce environments
Haibin Zhang, Yan Wang, Xiuzhen Zhang, and et al. 2015 · 2015
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Recommender systems—beyond matrix completion
Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker. 2016 · 2016
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Recommendations as treatments: debiasing learning and evaluation. In
Tobias Schnabel, Adith Swaminathan, and et al. 2016 · 2016
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Edge computing: vision and challenges
Weisong Shi and et al. 2016 · 2016
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Fairness-aware group recommendation with pareto-efficiency. In
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and et al. 2017 · 2017
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Systematically understanding the cyber attack business: a survey
Keman Huang, Michael Siegel, and Stuart Madnick. 2018 · 2018
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User fairness in recommender systems. In
Jurek Leonhardt and et al. 2018 · 2018
Cited alongside, same era.
Privacy enhanced matrix factorization for recommendation with local differential privacy
Hyejin Shin, Sungwook Kim, and et al. 2018 · 2018
Cited alongside, same era.
Transparent, scrutable and explainable user models for personalized recommendation. In
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019 · 2019
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, and et al. 2021 · 2021
Later among the works it cites.
Deep learning for anomaly detection: a review
Guansong Pang, Chunhua Shen, Longbing Cao, and et al. 2021 · 2021
Later among the works it cites.
A survey on session-based recommender systems
Shoujin Wang, Longbing Cao, Yan Wang, Quan Z Sheng, Mehmet A Orgun, and Defu Lian. 2021 · 2021
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Counterfactual data-augmented sequential recommendation. In
Zhenlei Wang and et al. 2021 · 2021
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A reliable deep representation learning to improve trust-aware recommendation systems
Milad Ahmadian, Mahmood Ahmadi, and Sajad Ahmadian. 2022 · 2022
Closest in time.
Recommender systems under European AI regulations
Tommaso Di Noia, Nava Tintarev, Panagiota Fatourou, and Markus Schedl. 2022 · 2022
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Cited alongside, same era.
Large scale adversarial representation learning. In
Jeff Donahue and Karen Simonyan. 2019 · 2019
Cited alongside, same era.
Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration
Wenpeng Lu, Fanqing Meng, Shoujin Wang, Guoqiang Zhang, Xu Zhang, Antai Ouyang, and Xiaodong Zhang. 2019 · 2019
Cited alongside, same era.
Learning disentangled representations for recommendation. In
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019 · 2019
Cited alongside, same era.
Federated learning
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, and Han Yu. 2019 · 2019
Cited alongside, same era.
Deep learning based recommender system: a survey and new perspectives
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019 · 2019
Cited alongside, same era.
Multistakeholder recommendation: Survey and research directions
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, and et al. 2020 · 2020
Cited alongside, same era.
Closest in time.
Definition of trustworthy
O. L. Dictionaries. 2022 · 2022
Closest in time.
A survey for trust-aware recommender systems: a deep learning perspective
Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, and Liming Zhu. 2022 · 2022
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A survey on trustworthy recommender systems
Yingqiang Ge, Shuchang Liu, Zuohui Fu, and et al. 2022a · 2022
Closest in time.
Recommender systems effect on the evolution of users’ choices distribution
Naieme Hazrati and Francesco Ricci. 2022 · 2022
Closest in time.
Federated social recommendation with graph neural network
Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu. 2022 · 2022
Closest in time.
Regulation rules on the recommendation algorithm for internet information service
Cyberspace Administration of China. 2022 · 2022
Closest in time.
Recommender Systems Handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). 2022 · 2022
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Veracity-aware and event-driven personalized news recommendation for fake news mitigation. In
Shoujin Wang, Xiaofei Xu, Xiuzhen Zhang, Yan Wang, and et al. 2022 · 2022
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A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation
Le Wu, Xiangnan He, and et al. 2022 · 2022
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Trustworthy graph neural networks: aspects, methods and trends
He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, and et al. 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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Cybersecurity capabilities and cyber-attacks as drivers of investment in cybersecurity systems: A UK survey for 2018 and 2019
Ignacio Fernandez De Arroyabe, Carlos FA Arranz, Marta F Arroyabe, and Juan Carlos Fernandez de Arroyabe. 2023 · 2023
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ReFRS: Resource-efficient federated recommender system for dynamic and diversified user preferences
Mubashir Imran, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen, Alexander Zhou, and Kai Zheng. 2023 · 2023
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A Survey on Fairness-aware Recommender Systems
Di Jin, Luzhi Wang, He Zhang, Yizhen Zheng, Weiping Ding, Feng Xia, and Shirui Pan. 2023 · 2023
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Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2023 · 2023
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Data science for next-generation recommender systems
Shoujin Wang, Yan Wang, Fikret Sivrikaya, Sahin Albayrak, and Vito Walter Anelli. 2023b · 2023
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A survey on the fairness of recommender systems
Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, and Shaoping Ma. 2023a · 2023
Closest in time.