<reponame>mlresearch/papersite
@InProceedings{karatzoglou10a,
  title = {Collaborative Filtering on a Budget},
  author = {<NAME> and <NAME> and <NAME>},
  pages = {389--396},
  abstract = {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 unbounded storage (at a graceful storage / performance   trade-off) for users and items to be represented in a pre-defined   memory footprint.  It allows us to scale recommender systems to very   large numbers of users or conversely, obtain very good performance   even for tiny models (e.g. 400kB of data suffice for a   representation of the EachMovie problem).    We provide both experimental results and approximation bounds for   our compressed representation and we show how this approach can be   extended to multipartite problems.},
  pdf = {http://jmlr.org/proceedings/papers/v9/karatzoglou10a/karatzoglou10a.pdf},
}
