MovieLens

GroupLens Research has collected and made available rating data sets from the MovieLens web site (http://movielens.org). The data sets were collected over various periods of time, depending on the size of the set. Before using these data sets, please review their README files for the usage licenses and other details.

Keywords for this software

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References in zbMATH (referenced in 58 articles )

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  1. Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.: Computing large market equilibria using abstractions (2022)
  2. Pfannschmidt, Karlson; Gupta, Pritha; Haddenhorst, Björn; Hüllermeier, Eyke: Learning context-dependent choice functions (2022)
  3. Anderson, Paul E.; Chartier, Timothy P.; Langville, Amy N.; Pedings-Behling, Kathryn E.: The rankability of weighted data from pairwise comparisons (2021)
  4. Atarashi, Kyohei; Oyama, Satoshi; Kurihara, Masahito: Factorization machines with regularization for sparse feature interactions (2021)
  5. Balasubramanian, Krishnakumar: Nonparametric modeling of higher-order interactions via hypergraphons (2021)
  6. Burashnikova, Aleksandra; Maximov, Yury; Clausel, Marianne; Laclau, Charlotte; Iutzeler, Franck; Amini, Massih-Reza: Learning over no-preferred and preferred sequence of items for robust recommendation (2021)
  7. Chen, Yunxiao; Ying, Zhiliang; Zhang, Haoran: Unfolding-model-based visualization: theory, method and applications (2021)
  8. Dong, Shuyu; Absil, P.-A.; Gallivan, K. A.: Riemannian gradient descent methods for graph-regularized matrix completion (2021)
  9. Garber, Dan: On the convergence of projected-gradient methods with low-rank projections for smooth convex minimization over trace-norm balls and related problems (2021)
  10. Gomez-Uribe, Carlos A.; Karrer, Brian: The decoupled extended Kalman filter for dynamic exponential-family factorization models (2021)
  11. Kadıoğlu, Serdar; Kleynhans, Bernard; Wang, Xin: Optimized item selection to boost exploration for recommender systems (2021)
  12. Rago, Antonio; Cocarascu, Oana; Bechlivanidis, Christos; Lagnado, David; Toni, Francesca: Argumentative explanations for interactive recommendations (2021)
  13. Ramanan, Nandini; Kunapuli, Gautam; Khot, Tushar; Fatemi, Bahare; Kazemi, Seyed Mehran; Poole, David; Kersting, Kristian; Natarajan, Sriraam: Structure learning for relational logistic regression: an ensemble approach (2021)
  14. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  15. Watanabe, Chihiro; Suzuki, Taiji: Selective inference for latent block models (2021)
  16. Babkin, Andrey: Incorporating side information into robust matrix factorization with Bayesian quantile regression (2020)
  17. Beck, Amir; Hallak, Nadav: On the convergence to stationary points of deterministic and randomized feasible descent directions methods (2020)
  18. Beckerleg, Melanie; Thompson, Andrew: A divide-and-conquer algorithm for binary matrix completion (2020)
  19. Cheng, Xiaoye; Zhang, Jingjing; Yan, Lu (Lucy): Understanding the impact of individual users’ rating characteristics on the predictive accuracy of recommender systems (2020)
  20. Connamacher, Harold; Pancha, Nikil; Liu, Rui; Ray, Soumya: \textscRankboost(+): an improvement to \textscRankboost (2020)

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