Collaborative Filtering on a Budget

@inproceedings{Karatzoglou2010CollaborativeFO,
  title={Collaborative Filtering on a Budget},
  author={Alexandros Karatzoglou and Alexander J. Smola and Markus Weimer},
  booktitle={AISTATS},
  year={2010}
}
Matrix factorization is a successful technique for building collaborative filtering systems. While it works well on a large range of problems, it is also known for requiring significant amounts of storage for each user or item to be added to the database. This is a problem whenever the collaborative filtering task is larger than the medium-sized Netflix Prize data. In this paper, we propose a new model for representing and compressing matrix factors via hashing. This allows for essentially… CONTINUE READING
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