@inproceedings{parra2009evaluation,
  abstract = {Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system. },
  author = {Parra, Denis and Brusilovsky, Peter},
  booktitle = {Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web},
  interhash = {03a51e24ecab3ad66fcc381980144fea},
  intrahash = {42773258c36ccf2f59749991518d1784},
  issn = {1613-0073},
  location = {Torino, Italy},
  month = jun,
  series = {CEUR Workshop Proceedings},
  title = {Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike},
  url = {http://ceur-ws.org/Vol-467/paper5.pdf},
  volume = 467,
  year = 2009
}

@book{manning2008introduction,
  abstract = {"Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." -- Publisher's description.},
  address = {New York},
  author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich},
  interhash = {2e574e46b7668a7268e7f02b46f4d9bb},
  intrahash = {9f4ab13e07b48b9723113aa74224be65},
  isbn = {9780521865715 0521865719},
  publisher = {Cambridge University Press},
  title = {Introduction to Information Retrieval},
  url = {http://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr_1_1?ie=UTF8&qid=1337379279&sr=8-1},
  year = 2008
}