@article{konstan2012recommender,
  abstract = {Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.},
  author = {Konstan, JosephA. and Riedl, John},
  doi = {10.1007/s11257-011-9112-x},
  interhash = {4bacbfddd599dc935450572bb03df2dc},
  intrahash = {f0dbad7662753cd4e0f162fbd7e7a8ca},
  issn = {0924-1868},
  journal = {User Modeling and User-Adapted Interaction},
  language = {English},
  number = {1-2},
  pages = {101-123},
  publisher = {Springer Netherlands},
  title = {Recommender systems: from algorithms to user experience},
  url = {http://dx.doi.org/10.1007/s11257-011-9112-x},
  volume = 22,
  year = 2012
}

@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
}

@incollection{ganter2010basic,
  address = {Berlin / Heidelberg},
  author = {Ganter, Bernhard},
  booktitle = {Formal Concept Analysis},
  doi = {10.1007/978-3-642-11928-6_22},
  editor = {Kwuida, Léonard and Sertkaya, Baris},
  interhash = {f44d214d7176b9183d2bf29b8efbdc00},
  intrahash = {1ab6ebc7e975a5b4019814bb7935f9bc},
  isbn = {978-3-642-11927-9},
  keyword = {Computer Science},
  pages = {312-340},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Two Basic Algorithms in Concept Analysis},
  url = {http://dx.doi.org/10.1007/978-3-642-11928-6_22},
  volume = 5986,
  year = 2010
}

@article{kuznetsov2002comparing,
  author = {Kuznetsov, Sergei O. and Obiedkov, Sergei A.},
  doi = {10.1080/09528130210164170},
  eprint = {http://www.tandfonline.com/doi/pdf/10.1080/09528130210164170},
  interhash = {8d58be3c9b7cec4767458436c70dc532},
  intrahash = {582910e7a14a80469b2cb328fa0d9884},
  journal = {Journal of Experimental & Theoretical Artificial Intelligence},
  number = {2-3},
  pages = {189-216},
  title = {Comparing performance of algorithms for generating concept lattices},
  url = {http://www.tandfonline.com/doi/abs/10.1080/09528130210164170},
  volume = 14,
  year = 2002
}