@inproceedings{karypis2001evaluation,
  abstract = {The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of N items that will be of interest to a certain user. User-based Collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. To address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.In this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.},
  acmid = {502627},
  address = {New York, NY, USA},
  author = {Karypis, George},
  booktitle = {Proceedings of the Tenth International Conference on Information and Knowledge Management},
  doi = {10.1145/502585.502627},
  interhash = {ad804add9a1dec7cb4df3c98fac7dc13},
  intrahash = {234c68832d68a4530e3ba8e2fb533043},
  isbn = {1-58113-436-3},
  location = {Atlanta, Georgia, USA},
  numpages = {8},
  pages = {247--254},
  publisher = {ACM},
  series = {CIKM '01},
  title = {Evaluation of Item-Based Top-N Recommendation Algorithms},
  url = {http://doi.acm.org/10.1145/502585.502627},
  year = 2001
}

@article{zhang2010personalized,
  abstract = {Personalized recommender systems are confronting great challenges of accuracy, diversification and novelty, especially when the data set is sparse and lacks accessorial information, such as user profiles, item attributes and explicit ratings. Collaborative tags contain rich information about personalized preferences and item contents, and are therefore potential to help in providing better recommendations. In this article, we propose a recommendation algorithm based on an integrated diffusion on user–item–tag tripartite graphs. We use three benchmark data sets, Del.icio.us, MovieLens and BibSonomy, to evaluate our algorithm. Experimental results demonstrate that the usage of tag information can significantly improve accuracy, diversification and novelty of recommendations.},
  author = {Zhang, Zi-Ke and Zhou, Tao and Zhang, Yi-Cheng},
  doi = {10.1016/j.physa.2009.08.036},
  interhash = {caa341f4d9ffb507dbf72f75a201dbd1},
  intrahash = {8fc27ade71ea065b92874ba29fca711b},
  issn = {0378-4371},
  journal = {Physica A: Statistical Mechanics and its Applications},
  number = 1,
  pages = {179 - 186},
  title = {Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs},
  url = {http://www.sciencedirect.com/science/article/pii/S0378437109006839},
  volume = 389,
  year = 2010
}

@electronic{priem2010scientometrics,
  abstract = {The growing flood of scholarly literature is exposing the weaknesses of current, citation-based methods of evaluating and filtering articles. A novel and promising approach is to examine the use and citation of articles in a new forum: Web 2.0 services like social bookmarking and microblogging. Metrics based on this data could build a “Scientometics 2.0,” supporting richer and more timely pictures of articles' impact. This paper develops the most comprehensive list of these services to date, assessing the potential value and availability of data from each. We also suggest the next steps toward building and validating metrics drawn from the social Web.},
  author = {Priem, Jason and Hemminger, Bradely H.},
  interhash = {d38dfec4da93265575aff99a811839d9},
  intrahash = {b95d32eed9419fefc007245914faad98},
  journal = {First Monday; Volume 15, Number 7 - 5 July 2010},
  title = {Scientometrics 2.0: New metrics of scholarly impact on the social Web},
  url = {http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2874/2570},
  year = 2010
}

@inproceedings{McNee:2006:AEA:1125451.1125659,
  abstract = {Recommender systems have shown great potential to help users find interesting and relevant items from within a large information space. Most research up to this point has focused on improving the accuracy of recommender systems. We believe that not only has this narrow focus been misguided, but has even been detrimental to the field. The recommendations that are most accurate according to the standard metrics are sometimes not the recommendations that are most useful to users. In this paper, we propose informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies. We propose new user-centric directions for evaluating recommender systems.},
  acmid = {1125659},
  address = {New York, NY, USA},
  author = {McNee, Sean M. and Riedl, John and Konstan, Joseph A.},
  booktitle = {CHI '06 extended abstracts on Human factors in computing systems},
  doi = {10.1145/1125451.1125659},
  interhash = {fe396fbce5daacd374196ad688e3f149},
  intrahash = {4b9fddbd766a9247856641989a778b23},
  isbn = {1-59593-298-4},
  location = {Montr\&\#233;al, Qu\&\#233;bec, Canada},
  numpages = {5},
  pages = {1097--1101},
  publisher = {ACM},
  series = {CHI EA '06},
  title = {Being accurate is not enough: how accuracy metrics have hurt recommender systems},
  url = {http://doi.acm.org/10.1145/1125451.1125659},
  year = 2006
}

@article{gedikli2010rating,
  author = {Gedikli, Fatih and Jannach, Dietmar},
  interhash = {7a4e1b28558c54b576678146c5a614fe},
  intrahash = {e7380137d10bd6a765897ea54bd05a31},
  journal = {Systems and the Social Web at ACM },
  title = {Rating items by rating tags},
  year = 2010
}

@inproceedings{heck2011testing,
  author = {Heck, Tamara and Peters, Isabella and Stock, Wolfgang G.},
  booktitle = {Workshop on Recommender Systems and the Social Web (ACM RecSys'11)},
  interhash = {d250a0eb45ca7c198d9cdb238802fd74},
  intrahash = {8b68db4ae61ec5c97010fbec2ddaa6c6},
  title = {Testing collaborative filtering against co-citation analysis and bibliographic coupling for academic author recommendation},
  year = 2011
}

@presentation{noauthororeditor,
  author = {leaong, Sheryl},
  interhash = {94d316680af6c91206302e964f2d7918},
  intrahash = {03ec5a6b30883646ee0c489630656b04},
  title = {A survey of recommender systems for scientific papers},
  year = 2012
}