Most cited papers in Recommender Systems and Techniques
What are the most cited papers in Recommender Systems and Techniques?
The most cited paper in Recommender Systems and Techniques is "Matrix Factorization Techniques for Recommender Systems" (Yehuda Koren, Robert Bell, Chris T. Volinsky, 2009), with 11,958 citations. Next come "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions" (10,402) and "Item-based collaborative filtering recommendation algorithms" (9,115).
Showing 50 of 98 papers. Every paper with more than 1,092 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Matrix Factorization Techniques for Recommender Systems Yehuda Koren, Robert Bell, Chris T. Volinsky · Computer | Aug 2009 | 11,958 |
| 2 | Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions Gediminas Adomavičius, Alexander Tuzhilin · IEEE Transactions on Knowledge and Data Engineering | 25 Apr 2005 | 10,402 |
| 3 | Item-based collaborative filtering recommendation algorithms Badrul M. Sarwar, George Karypis, Joseph A. Konstan et al. | Apr 2001 | 9,115 |
| 4 | Neural Collaborative Filtering Xiangnan He, Lizi Liao, Hanwang Zhang et al. | 3 Apr 2017 | 6,831 |
| 5 | Evaluating collaborative filtering recommender systems Jonathan L. Herlocker, Joseph A. Konstan, Loren Terveen et al. · ACM Transactions on Information Systems | 2004 | 5,828 |
| 6 | Amazon.com recommendations: item-to-item collaborative filtering Greg Linden, Brent Smith, Jeremy C. York · IEEE Internet Computing | 2003 | 5,433 |
| 7 | GroupLens Paul Resnick, Neophytos Iacovou, Mitesh Suchak et al. | 1994 | 5,058 |
| 8 | Empirical Analysis of Predictive Algorithms for Collaborative Filtering John S. Breese, David E. Heckerman, Carl Myers Kadie · arXiv (Cornell University) | 30 Jan 2013 | 4,504 |
| 9 | LightGCN Xiangnan He, Kuan Deng, Xiang Wang et al. | 25 Jul 2020 | 4,418 |
| 10 | BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner et al. · arXiv (Cornell University) | 9 May 2012 | 4,399 |
| 11 | The MovieLens Datasets F. Maxwell Harper, Joseph A. Konstan · ACM Transactions on Interactive Intelligent Systems | 22 Dec 2015 | 3,966 |
| 12 | Factorization meets the neighborhood Yehuda Koren | 24 Aug 2008 | 3,964 |
| 13 | Hybrid Recommender Systems: Survey and Experiments Robin Burke · User Modeling and User-Adapted Interaction | Nov 2002 | 3,852 |
| 14 | Recommender systems Paul Resnick, Hal R. Varian · Communications of the ACM | Mar 1997 | 3,718 |
| 15 | A Survey of Collaborative Filtering Techniques Xiaoyuan Su, Taghi M. Khoshgoftaar · Advances in Artificial Intelligence | 27 Oct 2009 | 3,644 |
| 16 | Probabilistic Matrix Factorization Andriy Mnih, Ruslan Salakhutdinov · Neural Information Processing Systems | 3 Dec 2007 | 3,566 |
| 17 | Deep Neural Networks for YouTube Recommendations Paul Covington, Jay Adams, Emre Sargin | Sep 2016 | 3,536 |
| 18 | Wide & Deep Learning for Recommender Systems Heng-Tze Cheng, Levent Koç, Jeremiah Harmsen et al. | 15 Sep 2016 | 3,424 |
| 19 | Collaborative Filtering for Implicit Feedback Datasets Yifan Hu, Yehuda Koren, Chris T. Volinsky | Dec 2008 | 3,259 |
| 20 | Neural Graph Collaborative Filtering Xiang Wang, Xiangnan He, Meng Wang et al. | 18 Jul 2019 | 3,200 |
| 21 | Recommender systems survey Jesús Bobadilla, Fernando Ortega, Antonio Hernando et al. · Knowledge-Based Systems | 6 Apr 2013 | 3,172 |
| 22 | Factorization Machines Steffen Rendle | Dec 2010 | 3,131 |
| 23 | Self-Attentive Sequential Recommendation Wang-Cheng Kang, Julian McAuley | Nov 2018 | 3,000 |
| 24 | Graph Convolutional Neural Networks for Web-Scale Recommender Systems Rex Ying, Ruining He, Kaifeng Chen et al. | 19 Jul 2018 | 2,858 |
| 25 | Social information filtering Upendra Shardanand, Pattie Maes | 1995 | 2,821 |
| 26 | Factorizing personalized Markov chains for next-basket recommendation Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme | 26 Apr 2010 | 2,765 |
| 27 | Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning Qimai Li, Zhichao Han, Xiao-Ming Wu · Proceedings of the AAAI Conference on Artificial Intelligence | 29 Apr 2018 | 2,669 |
| 28 | A contextual-bandit approach to personalized news article recommendation Lihong Li, Wei Chu, John C. Langford et al. | 26 Apr 2010 | 2,613 |
| 29 | Content-Based Recommendation Systems Michael J. Pazzani, Daniel Billsus · Lecture notes in computer science | 14 May 2007 | 2,549 |
| 30 | An Algorithmic Framework for Performing Collaborative Filtering Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers et al. · ACM SIGIR Forum | 2 Aug 2017 | 2,545 |
| 31 | BERT4Rec Fei Long Sun, Jun Liu, Jian Feng Wu et al. | 3 Nov 2019 | 2,506 |
| 32 | GroupLens Joseph A. Konstan, Brad Miller, David A. Maltz et al. · Communications of the ACM | Mar 1997 | 2,386 |
| 33 | Introduction to Recommender Systems Handbook Francesco Ricci⋆, Lior Rokach, Bracha Shapira | 5 Oct 2010 | 2,344 |
| 34 | DeepFM: A Factorization-Machine based Neural Network for CTR Prediction Huifeng Guo, Ruiming Tang, Yunming Ye et al. | 28 Jul 2017 | 2,307 |
| 35 | Item-based top- N recommendation algorithms Mukund S. Deshpande, George Karypis · ACM Transactions on Information Systems | 2004 | 2,208 |
| 36 | KGAT Xiang Wang, Xiangnan He, Yixin Cao et al. | 25 Jul 2019 | 2,179 |
| 37 | Collaborative Filtering Recommender Systems J. Ben Schafer, Dan Frankowski, Jon Herlocker et al. · Lecture notes in computer science | 14 May 2007 | 2,129 |
| 38 | Web usage mining Jaideep Srivastava, Robert Cooley, Mukund S. Deshpande et al. · ACM SIGKDD Explorations Newsletter | 2000 | 2,112 |
| 39 | NewsWeeder: Learning to Filter Netnews Ken Lang · Elsevier eBooks | 1995 | 2,082 |
| 40 | Methods and techniques of adaptive hypermedia Peter L. Brusilovsky · User Modeling and User-Adapted Interaction | Jul 1996 | 2,041 |
| 41 | Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding Jiaxi Tang, Ke Wang | 2 Feb 2018 | 2,015 |
| 42 | Adaptive Hypermedia Peter L. Brusilovsky · User Modeling and User-Adapted Interaction | Mar 2001 | 2,001 |
| 43 | Graph Neural Networks for Social Recommendation Wenqi Fan, Yao Ma, Qing Li et al. | 13 May 2019 | 1,993 |
| 44 | SimRank Glen Jeh, Jennifer Widom | 23 Jul 2002 | 1,959 |
| 45 | Content-based Recommender Systems: State of the Art and Trends Pasquale Lops, Marco de Gemmis, Giovanni Maria Semeraro | 5 Oct 2010 | 1,905 |
| 46 | Restricted Boltzmann machines for collaborative filtering Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hinton | 20 Jun 2007 | 1,892 |
| 47 | Analysis of recommendation algorithms for e-commerce Badrul M. Sarwar, George Karypis, Joseph A. Konstan et al. | 17 Oct 2000 | 1,837 |
| 48 | Usage patterns of collaborative tagging systems Scott A. Golder, Bernardo A. Huberman · Journal of Information Science | 21 Mar 2006 | 1,833 |
| 49 | Collaborative Knowledge Base Embedding for Recommender Systems Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian et al. | 8 Aug 2016 | 1,819 |
| 50 | Methods and metrics for cold-start recommendations Andrew I. Schein, Alexandrin Popescul, Lyle Ungar et al. | 11 Aug 2002 | 1,777 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.