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Recommender systems operate in an inherently dynamical setting.
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Springer New York, New York, NY, 2008
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Factorization meets the neighborhood: a multifaceted collaborative filtering model
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Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity
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Collaborative filtering with temporal dynamics
Y. Koren · 2009
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Matrix factorization techniques for recommender systems
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A contextual-bandit approach to personalized news article recommendation
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Contextual bandits with linear payoff functions
W. Chu, L. Li, L. Reyzin, and R. Schapire · 2011
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Factorization machines with libFM
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Do recommender systems manipulate consumer preferences? a study of anchoring effects
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Biased assimilation, homophily, and the dynamics of polarization
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Playing atari with deep reinforcement learning
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A theoretical analysis of ndcg type ranking measures
Y. Wang, L. Wang, Y. Li, D. He, and T.-Y. Liu · 2013
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Exploring the filter bubble: the effect of using recommender systems on content diversity
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A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems
J. Beel and S. Langer · 2015
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Time-aware recommender systems: A comprehensive survey and analysis of existing evaluation protocols
P. Campos, F. Díez, and I. Cantador · 2015
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The movielens datasets: History and context
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Just in time recommendations: Modeling the dynamics of boredom in activity streams
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Efficient thompson sampling for online matrix-factorization recommendation
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Predicting online performance of news recommender systems through richer evaluation metrics
How algorithmic confounding in recommendation systems increases homogeneity and decreases utility
A. J. Chaney, B. M. Stewart, and B. E. Engelhardt · 2018
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D. Rohde, S. Bonner, T. Dunlop, F. Vasile, and A. Karatzoglou · 2018
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Human interaction with recommendation systems
S. Schmit and C. Riquelme · 2018
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Fairness of exposure in rankings
A. Singh and T. Joachims · 2018
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Fighting boredom in recommender systems with linear reinforcement learning
R. Warlop, A. Lazaric, and J. Mary · 2018
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A. Maksai, F. Garcin, and B. Faltings · 2015
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Forgetting methods for incremental matrix factorization in recommender systems
P. Matuszyk, J. Vinagre, M. Spiliopoulou, A. M. Jorge, and J. Gama · 2015
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Autorec: Autoencoders meet collaborative filtering
S. Sedhain, A. K. Menon, S. Sanner, and L. Xie · 2015
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An overview on the exploitation of time in collaborative filtering
J. Vinagre, A. M. Jorge, and J. Gama · 2015
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Deep neural networks for youtube recommendations
P. Covington, J. Adams, and E. Sargin · 2016
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Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems
M. Kaminskas and D. Bridge · 2016
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Llorma: Local low-rank matrix approximation
J. Lee, S. Kim, G. Lebanon, Y. Singer, and S. Bengio · 2016
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C. Berner, G. Brockman, B. Chan, V. Cheung, P. Debiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, et al · 2019
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Top-k off-policy correction for a reinforce recommender system
M. Chen, A. Beutel, P. Covington, S. Jain, F. Belletti, and E. H. Chi · 2019
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Are we really making much progress? a worrying analysis of recent neural recommendation approaches
M. F. Dacrema, P. Cremonesi, and D. Jannach · 2019
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Degenerate feedback loops in recommender systems
R. Jiang, S. Chiappa, T. Lattimore, A. György, and P. Kohli · 2019
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Offline and online evaluation of recommendations
A. B. Kouki and A. Said · 2019
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Predicting online performance of job recommender systems with offline evaluation
A. Mogenet, T. A. N. Pham, M. Kazama, and J. Kong · 2019
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Do imagenet classifiers generalize to imagenet?
B. Recht, R. Roelofs, L. Schmidt, and V. Shankar · 2019
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On the difficulty of evaluating baselines: A study on recommender systems
S. Rendle, L. Zhang, and Y. Koren · 2019
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Embarrassingly shallow autoencoders for sparse data
H. Steck · 2019
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Recommendations and user agency: the reachability of collaboratively-filtered information
S. Dean, S. Rich, and B. Recht · 2020
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A longitudinal analysis of youtube’s promotion of conspiracy videos
M. Faddoul, G. Chaslot, and H. Farid · 2020
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Understanding echo chambers in e-commerce recommender systems
Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, and Y. Zhang · 2020
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Feedback loop and bias amplification in recommender systems
M. Mansoury, H. Abdollahpouri, M. Pechenizkiy, B. Mobasher, and R. Burke · 2020
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The effect of natural distribution shift on question answering models
J. Miller, K. Krauth, B. Recht, and L. Schmidt · 2020
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Unbiased recommender learning from missing-not-at-random implicit feedback
Y. Saito, S. Yaginuma, Y. Nishino, H. Sakata, and K. Nakata · 2020
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