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Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of accuracy measures.
GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In
Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994 · 1994
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Improvements That Don’t Add Up: Ad-hoc Retrieval Results Since 1998. In
Timothy G. Armstrong, Alistair Moffat, William Webber, and Justin Zobel. 2009 · 1998
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Learning Collaborative Information Filters. In
Daniel Billsus and Michael J. Pazzani. 1998 · 1998
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Item-Based Collaborative Filtering Recommendation Algorithms. In
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001 · 2001
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Amazon.com Recommendations: Item-to-Item Collaborative Filtering
Greg Linden, Brent Smith, and Jeremy York. 2003 · 2003
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Item-based top-
Mukund Deshpande and George Karypis. 2004 · 2004
Earlier work this paper cites.
Collaborative Filtering for Implicit Feedback Datasets. In
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008 · 2008
Earlier work this paper cites.
BPR: Bayesian Personalized Ranking from Implicit Feedback. In
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
Performance of recommender algorithms on top-n recommendation tasks. In
Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010 · 2010
Earlier work this paper cites.
Algorithms for Hyper-Parameter Optimization. In
James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. 2011 · 2011
Earlier work this paper cites.
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In
Michael D. Ekstrand, Michael Ludwig, Joseph A. Konstan, and John Riedl. 2011 · 2011
Earlier work this paper cites.
SLIM: Sparse Linear Methods for Top-N Recommender Systems. In
Xia Ning and George Karypis. 2011 · 2011
Earlier work this paper cites.
Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In
Saúl Vargas and Pablo Castells. 2011 · 2011
Earlier work this paper cites.
Efficient top-n recommendation by linear regression. In
Mark Levy and Kris Jack. 2013 · 2013
Cited alongside, same era.
Research Commentary—Too Big to Fail: Large Samples and the p-Value Problem
Mingfeng Lin, Henry C. Lucas, and Galit Shmueli. 2013 · 2013
Cited alongside, same era.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2015 · 2015
Cited alongside, same era.
What recommenders recommend: an analysis of recommendation biases and possible countermeasures
Dietmar Jannach, Lukas Lerche, Iman Kamehkhosh, and Michael Jugovac. 2015 · 2015
Cited alongside, same era.
Image-Based Recommendations on Styles and Substitutes. In
Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Cited alongside, same era.
Local Item-Item Models For Top-N Recommendation. In
Evangelia Christakopoulou and George Karypis. 2016 · 2016
Embarrassingly Shallow Autoencoders for Sparse Data. In
Harald Steck. 2019 · 2019
Later among the works it cites.
Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?. In
Dietmar Jannach, Gabriel De Souza P. Moreira, and Even Oldridge. 2020 · 2020
Later among the works it cites.
On Sampled Metrics for Item Recommendation. In
Walid Krichene and Steffen Rendle. 2020 · 2020
Later among the works it cites.
Neural Collaborative Filtering vs. Matrix Factorization Revisited. In
Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020 · 2020
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Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. In
Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, and Cong Geng. 2020 · 2020
Later among the works it cites.
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. In
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Cited alongside, same era.
Neural collaborative filtering. In
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Cited alongside, same era.
Updatable, Accurate, Diverse, and Scalable Recommendations for Interactive Applications
Bibek Paudel, Fabian Christoffel, Chris Newell, and Abraham Bernstein. 2017 · 2017
Cited alongside, same era.
Variational Autoencoders for Collaborative Filtering. In
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
On the robustness and discriminative power of information retrieval metrics for top-N recommendation. In
Daniel Valcarce, Alejandro Bellogín, Javier Parapar, and Pablo Castells. 2018 · 2018
Cited alongside, same era.
Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking. In
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2019 · 2019
Cited alongside, same era.
On the discriminative power of hyper-parameters in cross-validation and how to choose them. In
Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, and Azzurra Ragone. 2019 · 2019
Cited alongside, same era.
Ziwei Zhu, Jianling Wang, and James Caverlee. 2020 · 2020
Later among the works it cites.
Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. In
Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021 · 2021
Later among the works it cites.
Progress in recommender systems research: Crisis? What crisis?
Paolo Cremonesi and Dietmar Jannach. 2021 · 2021
Later among the works it cites.
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021 · 2021
Later among the works it cites.
Session-aware Recommendation: A Surprising Quest for the State-of-the-art
Sara Latifi, Noemi Mauro, and Dietmar Jannach. 2021 · 2021
Later among the works it cites.
iALS++: Speeding up Matrix Factorization with Subspace Optimization
Steffen Rendle, Walid Krichene, Li Zhang, and Yehuda Koren. 2021a · 2021
Later among the works it cites.
Revisiting the Performance of iALS on Item Recommendation Benchmarks
Steffen Rendle, Walid Krichene, Li Zhang, and Yehuda Koren. 2021b · 2021
Later among the works it cites.
Deep Learning for Recommender Systems: A Netflix Case Study
Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, and Justin Basilico. 2021 · 2021
Later among the works it cites.