Matrix factorization and neighbor based algorithms for the netflix prize problem

@inproceedings{Takcs2008MatrixFA,
  title={Matrix factorization and neighbor based algorithms for the netflix prize problem},
  author={G. Tak{\'a}cs and I. Pil{\'a}szy and B. N{\'e}meth and D. Tikk},
  booktitle={RecSys '08},
  year={2008}
}
Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. [...] Key Method First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coefficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or…Expand
157 Citations
SBMF: Similarity-Based Matrix Factorization for Collaborative Recommendation
  • Xin Wang, Congfu Xu
  • Computer Science
  • 2014 IEEE 26th International Conference on Tools with Artificial Intelligence
  • 2014
The Impact of Basic Matrix Factorization Refinements on Recommendation Accuracy
Generating Pseudotransactions for Improving Sparse Matrix Factorization
Scaling Collaborative Filtering with PETSc
  • A. Johnson
  • Computer Science
  • 2018 IEEE International Conference on Big Data (Big Data)
  • 2018
A hybrid recommendation approach using LDA and probabilistic matrix factorization
Matrix and Tensor Decomposition in Recommender Systems
...
1
2
3
4
5
...