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{\'a}bor Tak{\'a}cs and Istv{\'a}n Pil{\'a}szy and Botty{\'a}n N{\'e}meth and Domonkos Tikk},
  booktitle={RecSys},
  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. This subfield of machine learning has gained a lot of popularity with the Netflix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the… CONTINUE READING
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Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

Seventh IEEE International Conference on Data Mining (ICDM 2007) • 2007
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