@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
}

@article{shani2011evaluating,
  author = {Shani, G. and Gunawardana, A.},
  interhash = {c93599e113544cde3f44502c88775c20},
  intrahash = {63a1a401a35be851b9864966184c6815},
  journal = {Recommender Systems Handbook},
  pages = {257--297},
  publisher = {Springer},
  title = {Evaluating recommendation systems},
  url = {http://scholar.google.de/scholar.bib?q=info:AW2lmZl44hMJ:scholar.google.com/&output=citation&hl=de&as_sdt=0,5&ct=citation&cd=0},
  year = 2011
}

@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},
  publisher = {ACM},
  title = {Evaluating collaborative filtering recommender systems},
  url = {http://doi.acm.org/10.1145/963770.963772},
  volume = 22,
  year = 2004
}

@inproceedings{abel_CIKM_2008,
  abstract = {Folksonomy systems have shown to contribute to the quality of Web search ranking strategies. In this paper, we analyze and compare different graph-based ranking algorithms, namely FolkRank, SocialPageRank, and SocialSimRank. We enhance these algorithms by exploiting the context of tag assignmets, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity delivers valuable semantic information about resources and their context. We show how to use this information to improve the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.},
  address = {New York, NY, USA},
  author = {Abel, Fabian and Henze, Nicola and Krause, Daniel},
  booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining},
  citeulike-article-id = {3500798},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1458082.1458316},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1458082.1458316},
  doi = {10.1145/1458082.1458316},
  interhash = {5d6db50409eef97339b135ab8f703538},
  intrahash = {d6d72db224fb84c0b4265f09111483e0},
  isbn = {978-1-59593-991-3},
  location = {Napa Valley, California, USA},
  pages = {1429--1430},
  posted-at = {2009-12-07 00:16:11},
  priority = {2},
  publisher = {ACM},
  title = {Ranking in folksonomy systems: can context help?},
  url = {http://dx.doi.org/10.1145/1458082.1458316},
  year = 2008
}

@misc{citeulike:305755,
  abstract = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamical aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given url. We also present a dynamical model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.},
  author = {Golder, Scott and Huberman, Bernardo A.},
  citeulike-article-id = {305755},
  eprint = {cs.DL/0508082},
  interhash = {2d312240f16eba52c5d73332bc868b95},
  intrahash = {f852d7a909fa3edceb04abb7d2a20f71},
  month = Aug,
  title = {The Structure of Collaborative Tagging Systems},
  url = {http://arxiv.org/abs/cs.DL/0508082},
  year = 2005
}