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Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy.
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Item-based collaborative filtering recommendation algorithms. In Proceedings of the Tenth International World Wide Web Conference, WWW 10, Hong Kong, China, May 1-5, 2001 , Vincent Y. Shen, Nobuo Saito, Michael R. Lyu, and Mary Ellen Zurko (Eds.). ACM, 285–295
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Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
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Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web, WWW 2004, New York, NY, USA, May 17-20, 2004 , Stuart I. Feldman, Mike Uretsky, Marc Najork, and Craig E. Wills (Eds.). ACM, 393–402
Shyong K. Lam and John Riedl. 2004 · 2004
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Collaborative recommendation: A robustness analysis
Michael P. O’Mahony, Neil J. Hurley, Nicholas Kushmerick, and Guenole C. M. Silvestre. 2004 · 2004
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Identifying attack models for secure recommendation
Robin Burke, Bamshad Mobasher, Roman Zabicki, and Runa Bhaumik. 2005 · 2005
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Preventing shilling attacks in online recommender systems. In Seventh ACM International Workshop on Web Information and Data Management (WIDM 2005), Bremen, Germany, November 4, 2005 , Angela Bonifati and Dongwon Lee (Eds.). ACM, 67–74
Paul-Alexandru Chirita, Wolfgang Nejdl, and Cristian Zamfir. 2005 · 2005
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Effective attack models for shilling item-based collaborative filtering systems. Citeseer
Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2005 · 2005
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Recommender Systems: Attack Types and Strategies. In Proceedings, The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference, July 9-13, 2005, Pittsburgh, Pennsylvania, USA , Manuela M. Veloso and Subbarao Kambhampati (Eds.). AAAI Press / The MIT Press, 334–339
Michael P. O’Mahony, Neil J. Hurley, and Guenole C. M. Silvestre. 2005 · 2005
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Securing collaborative filtering against malicious attacks through anomaly detection. In Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization (ITWP’06), Boston , Vol. 6. 10
Runa Bhaumik, Chad Williams, Bamshad Mobasher, and Robin Burke. 2006 · 2006
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Model-based collaborative filtering as a defense against profile injection attacks. In AAAI , Vol. 6. 1388
Bamshad Mobasher, Robin Burke, and Jeff J Sandvig. 2006 · 2006
Cited alongside, same era.
Attacks and Remedies in Collaborative Recommendation
Bamshad Mobasher, Robin D. Burke, Runa Bhaumik, and Jeff J. Sandvig. 2007a · 2007
Cited alongside, same era.
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
Bamshad Mobasher, Robin D. Burke, Runa Bhaumik, and Chad Williams. 2007b · 2007
Cited alongside, same era.
Attack resistant collaborative filtering. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, Singapore, July 20-24, 2008 , Sung-Hyon Myaeng, Douglas W. Oard, Fabrizio Sebastiani, Tat-Seng Chua, and Mun-Kew Leong (Eds.). ACM, 75–82
Bhaskar Mehta and Wolfgang Nejdl. 2008 · 2008
Cited alongside, same era.
Unsupervised strategies for shilling detection and robust collaborative filtering
Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges
Yue Shi, Martha Larson, and Alan Hanjalic. 2014 · 2014
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Addressing cold start in recommender systems: A semi-supervised co-training algorithm. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval . ACM, 73–82
Mi Zhang, Jie Tang, Xuchen Zhang, and Xiangyang Xue. 2014 · 2014
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Recommender Systems in Industry: A Netflix Case Study
Xavier Amatriain and Justin Basilico. 2015 · 2015
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Recommender Systems - The Textbook
Charu C. Aggarwal. 2016 · 2016
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The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2016 · 2016
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Bhaskar Mehta and Wolfgang Nejdl. 2009 · 2009
Cited alongside, same era.
BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18-21, 2009 , Jeff A. Bilmes and Andrew Y. Ng (Eds.). AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Cited alongside, same era.
Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization. In 22nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2010, Arras, France, 27-29 October 2010 - Volume 2 . IEEE Computer Society, 105–112
Zunping Cheng and Neil Hurley. 2010 · 2010
Cited alongside, same era.
SLIM: Sparse Linear Methods for Top-N Recommender Systems. In 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, December 11-14, 2011 , Diane J. Cook, Jian Pei, Wei Wang, Osmar R. Zaïane, and Xindong Wu (Eds.). IEEE Computer Society, 497–506
Xia Ning and George Karypis. 2011 · 2011
Cited alongside, same era.
Improving Stability of Recommender Systems: A Meta-Algorithmic Approach
Gediminas Adomavicius and Jingjing Zhang. 2015 · 2014
Cited alongside, same era.
Shilling attacks against recommender systems: a comprehensive survey
Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014 · 2014
Cited alongside, same era.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016 , Raffaele Perego, Fabrizio Sebastiani, Javed A. Aslam, Ian Ruthven, and Justin Zobel (Eds.). ACM, 549–558
Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016 · 2016
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Detecting abnormal profiles in collaborative filtering recommender systems
Zhihai Yang and Zhongmin Cai. 2017 · 2017
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Content-Based Multimedia Recommendation Systems: Definition and Application Domains. In Proceedings of the 9th Italian Information Retrieval Workshop, Rome, Italy, May, 28-30, 2018. (CEUR Workshop Proceedings) , Nicola Tonellotto, Luca Becchetti, and Marko Tkalcic (Eds.), Vol. 2140. CEUR-WS.org
Yashar Deldjoo, Markus Schedl, Paolo Cremonesi, and Gabriella Pasi. 2018 · 2018
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Adversarial Personalized Ranking for Recommendation. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018 , Kevyn Collins-Thompson, Qiaozhu Mei, Brian D. Davison, Yiqun Liu, and Emine Yilmaz (Eds.). ACM, 355–364
Xiangnan He, Zhankui He, Xiaoyu Du, and Tat-Seng Chua. 2018 · 2018
Later among the works it cites.
Movie genome: alleviating new item cold start in movie recommendation
Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, and Paolo Cremonesi. 2019 · 2019
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