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 )

Showing results 41 to 58 of 58.
Sorted by year (citations)
  1. Taleghani, Rahman; Salahi, Maziar: An ADMM-factorization algorithm for low rank matrix completion (2019)
  2. Wu, Xiaolin; Cao, Fuyuan: An unsupervised outlier detection algorithm for categorical matrix-object data (2019)
  3. Yang, Jing; Li, Xiaoye; Sun, Zhenlong; Zhang, Jianpei: A differential privacy framework for collaborative filtering (2019)
  4. Alviano, Mario; Greco, Gianluigi; Guzzo, Antonella: Coalition formation in social environments with logic-based agents (2018)
  5. de Sá, Cláudio Rebelo; Duivesteijn, Wouter; Azevedo, Paulo; Jorge, Alípio Mário; Soares, Carlos; Knobbe, Arno: Discovering a taste for the unusual: exceptional models for preference mining (2018)
  6. Khrulkov, Valentin; Oseledets, Ivan: Desingularization of bounded-rank matrix sets (2018)
  7. Huang, Jiajin; Wang, Jian; Yao, Yiyu; Zhong, Ning: Cost-sensitive three-way recommendations by learning pair-wise preferences (2017)
  8. Schibler, Thomas; Suri, Subhash: (k)-dominance in multidimensional data: theory and applications (2017)
  9. Higham, Nicholas J.; Relton, Samuel D.: Estimating the largest elements of a matrix (2016)
  10. Kim, Mijung; Candan, K. Selçuk: Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and Tucker decompositions (2016)
  11. Lazzaro, Damiana: A nonconvex approach to low-rank matrix completion using convex optimization. (2016)
  12. Nath, Swaprava; Sandholm, Tuomas: Efficiency and budget balance (2016)
  13. Jung, Jason J.; Pham, Xuan Hau: Attribute selection-based recommendation framework for long-tail user group: an empirical study on MovieLens dataset (2011) ioport
  14. Mehta, Bhaskar; Nejdl, Wolfgang: Unsupervised strategies for shilling detection and robust collaborative filtering (2009) ioport
  15. Ahn, Hyung Jun: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem (2008) ioport
  16. Wang, Haixun; Pei, Jian: Clustering by pattern similarity (2008) ioport
  17. Lee, Hong Joo; Kim, Jong Woo; Park, Sung Joo: Understanding collaborative filtering parameters for personalized recommendations in e-commerce (2007)
  18. Leung, Cane Wing-Ki; Chan, Stephen Chi-Fai; Chung, Fu-Lai: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity (2006) ioport