Item-based collaborative filtering recommendation algorithms B. Sarwar, G. Karypis, J. Konstan, J. Riedl 2001 9400 31 Introduces an item-based approach to collaborative filtering, offering scalability and accuracy improvements. Matrix Factorization Techniques for Recommender Systems Y. Koren, Robert M. Bell, C. Volinsky 2009 9309 12 Discusses various matrix factorization techniques for recommendation systems, enhancing prediction accuracy. Neural Collaborative Filtering Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua 2017 5529 51 Proposes a neural network-based approach to collaborative filtering, capturing complex user-item interactions. BPR: Bayesian Personalized Ranking from Implicit Feedback Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, L. Schmidt-Thieme 2009 5378 18 Introduces Bayesian Personalized Ranking for learning from implicit feedback in recommendation systems. Probabilistic Matrix Factorization R. Salakhutdinov, A. Mnih 2007 4462 11 Presents a probabilistic approach to matrix factorization for predicting user preferences. Collaborative Filtering for Implicit Feedback Datasets Yifan Hu, Y. Koren, C. Volinsky 2008 3216 22 Explores collaborative filtering techniques for datasets with implicit feedback, using weighted matrix factorization. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, Meng Wang 2020 2995 54 Introduces a lightweight graph convolutional network for efficient and accurate recommendations. Neural Graph Collaborative Filtering Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, Tat-Seng Chua 2019 2570 56 Develops a graph neural network framework for collaborative filtering in recommendation systems. Factorizing personalized Markov chains for next-basket recommendation Steffen Rendle, Christoph Freudenthaler, L. Schmidt-Thieme 2010 2183 12 Proposes a method for next-basket recommendation using personalized Markov chains. Collaborative Filtering with Temporal Dynamics Y. Koren 2010 2064 30 Extends collaborative filtering to account for temporal changes in user preferences. Performance of recommender algorithms on top-N recommendation tasks P. Cremonesi, Y. Koren, R. Turrin 2010 1446 19 Evaluates various recommender algorithms on top-N recommendation tasks, providing insights into their performance. Advances in Collaborative Filtering Y. Koren, Robert M. Bell 2011 1358 43 Reviews recent advancements in collaborative filtering techniques and their applications. Variational Autoencoders for Collaborative Filtering Dawen Liang, R. G. Krishnan, M. Hoffman, Tony Jebara 2018 1138 54 Applies variational autoencoders to model user preferences in collaborative filtering. One-Class Collaborative Filtering Rong Pan, Yunhong Zhou, Bin Cao, N. Liu, R. Lukose, Martin Scholz, Qiang Yang 2008 1025 36 Introduces a one-class collaborative filtering method for scenarios with only positive feedback. A Comprehensive Survey of Neighborhood-based Recommendation Methods Christian Desrosiers, G. Karypis 2021 948 94 Provides an extensive review of neighborhood-based recommendation methods and their effectiveness. Lessons from the Netflix Prize Challenge Robert M. Bell, Y. Koren 2007 817 9 Reflects on the lessons learned from the Netflix Prize challenge, emphasizing ensemble methods and parameter tuning. Factorization meets the neighborhood: a multifaceted collaborative filtering model Y. Koren 2008 4104 26 Combines matrix factorization with neighborhood models for a more comprehensive collaborative filtering approach. Wide & Deep Learning for Recommender Systems Heng-Tze Cheng, L. Koc, Jeremiah Harmsen, T. Shaked, Tushar Chandra, H. Aradhye, Glen Anderson, G. Corrado, Wei Chai, M. Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah 2016 3429 8 Proposes a hybrid model combining wide linear models and deep neural networks for recommendation. Graph Convolutional Neural Networks for Web-Scale Recommender Systems Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, J. Leskovec 2018 3232 33 Applies graph convolutional neural networks to large-scale recommendation systems. Factorization Machines Steffen Rendle 2010 2588 16 Introduces factorization machines, a general-purpose algorithm for prediction problems. Self-Attentive Sequential Recommendation Wang-Cheng Kang, Julian McAuley 2018 2003 49 Utilizes self-attention mechanisms for sequential recommendation to capture user preferences. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang 2019 1805 61 Applies BERT for sequential recommendation, improving context understanding. KGAT: Knowledge Graph Attention Network for Recommendation Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua 2019 1621 42 Integrates knowledge graphs with attention networks for enhanced recommendation accuracy. Collaborative Deep Learning for Recommender Systems Hao Wang, Naiyan Wang, D. Yeung 2014 1584 45 Combines collaborative filtering with deep learning for improved recommendation systems. Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding Jiaxi Tang, Ke Wang 2018 1498 35 Proposes a convolutional sequence embedding for personalized top-N sequential recommendations. Graph Convolutional Matrix Completion Rianne van den Berg, Thomas Kipf, M. Welling 2017 1229 33 Uses graph convolutional networks for matrix completion in recommendation systems. AutoRec: Autoencoders Meet Collaborative Filtering Suvash Sedhain, A. Menon, S. Sanner, Lexing Xie 2015 1109 5 Introduces AutoRec, an autoencoder-based collaborative filtering model. Fast Matrix Factorization for Online Recommendation with Implicit Feedback Xiangnan He, Hanwang Zhang, Min-Yen Kan, Tat-Seng Chua 2016 974 33 Presents a fast matrix factorization method for online recommendation with implicit feedback. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems Yao Wu, Christopher DuBois, A. Zheng, M. Ester 2016 938 32 Applies denoising auto-encoders for top-N recommendations in collaborative filtering. Self-supervised Graph Learning for Recommendation Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie 2020 915 60 Explores self-supervised learning on graphs for recommendation systems. Deep Matrix Factorization Models for Recommender Systems Hong Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, Jiajun Chen 2017 834 33 Develops deep matrix factorization models to enhance recommendation accuracy. Would love to be able to sort this table by column vals. Want to use these papers to build a mesh around the YT algo, Tiktok algo, and pytorch concepts. 