@inproceedings{ekstrand2010automatically,
  abstract = {All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.},
  acmid = {1864740},
  address = {New York, NY, USA},
  author = {Ekstrand, Michael D. and Kannan, Praveen and Stemper, James A. and Butler, John T. and Konstan, Joseph A. and Riedl, John T.},
  booktitle = {Proceedings of the fourth ACM conference on Recommender systems},
  doi = {10.1145/1864708.1864740},
  interhash = {71ea85067f7d5f46bbb3a5da7e18ba34},
  intrahash = {fbe0d5fca62781e5156d04e20d324a46},
  isbn = {978-1-60558-906-0},
  location = {Barcelona, Spain},
  numpages = {8},
  pages = {159--166},
  publisher = {ACM},
  title = {Automatically building research reading lists},
  url = {http://doi.acm.org/10.1145/1864708.1864740},
  year = 2010
}

@article{Herlocker:2004:ECF:963770.963772,
  abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.},
  acmid = {963772},
  address = {New York, NY, USA},
  author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.},
  doi = {10.1145/963770.963772},
  interhash = {f8a70731d983634ac7105896d101c9d2},
  intrahash = {c3a659108a568db1fba183c680dd1fd2},
  issn = {1046-8188},
  issue = {1},
  journal = {ACM Trans. Inf. Syst.},
  month = {January},
  numpages = {49},
  pages = {5--53},
  privnote = {bla bla},
  publisher = {ACM},
  title = {Evaluating collaborative filtering recommender systems},
  url = {http://doi.acm.org/10.1145/963770.963772},
  volume = 22,
  year = 2004
}

@article{herlocker2004evaluating,
  abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.},
  address = {New York, NY, USA},
  author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Terveen, Loren G. and Riedl, John T.},
  doi = {http://doi.acm.org/10.1145/963770.963772},
  interhash = {f8a70731d983634ac7105896d101c9d2},
  intrahash = {bdd3980bb3c297d1b84ceb0c7729d397},
  issn = {1046-8188},
  journal = {ACM Trans. Inf. Syst.},
  number = 1,
  pages = {5--53},
  publisher = {ACM Press},
  title = {Evaluating collaborative filtering recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=963770.963772},
  volume = 22,
  year = 2004
}