@article{ls_leimeister,
  author = {Riedl, C. and Blohm, I. and Leimeister, J. M. and Krcmar, H.},
  interhash = {ddd9e70f7a541054cf6d3363e88ad5be},
  intrahash = {104120e1797eeca304affd2aff7106ec},
  journal = {International Journal of Electronic Commerce (IJEC)},
  note = 359,
  number = {17(3)},
  pages = {7-36},
  title = {The Effect of Rating Scales on Decision Quality and User Attitudes in Online Innovation Communities},
  url = {http://pubs.wi-kassel.de/wp-content/uploads/2013/04/JML_378.pdf},
  year = 2013
}

@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{ls_leimeister,
  address = {St. Louis, MO, USA},
  author = {Riedl, C. and Blohm, I. and Leimeister, J. M. and Krcmar, H.},
  booktitle = {30. First International Conference on Information Systems (ICIS) 2010},
  interhash = {07418ee3758f0a35c9d9e2c6c629a057},
  intrahash = {2ad4511439d17f4a38eb8b6515b2abe7},
  note = {203 (51-10)},
  number = 31,
  title = {Rating Scales for Collective Intelligence in Innovation Communities: Why Quick and Easy Decision Making Does Not Get it Right},
  url = {http://www.uni-kassel.de/fb7/ibwl/leimeister/pub/JML_203.pdf},
  year = 2010
}

@article{hirsch2005index,
  abstract = {I propose the index h, defined as the number of papers with citation number ≥h, as a useful index to characterize the scientific output of a researcher.},
  author = {Hirsch, J. E.},
  doi = {10.1073/pnas.0507655102},
  eprint = {http://www.pnas.org/content/102/46/16569.full.pdf+html},
  interhash = {e45cbc449d42e1841c704f121ec47f24},
  intrahash = {7773c451332a1a0a25313461bee7e045},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  number = 46,
  pages = {16569-16572},
  title = {An index to quantify an individual's scientific research output},
  url = {http://www.pnas.org/content/102/46/16569.abstract},
  volume = 102,
  year = 2005
}

@inproceedings{Dellarocas:2000:IOR:352871.352889,
  acmid = {352889},
  address = {New York, NY, USA},
  author = {Dellarocas, Chrysanthos},
  booktitle = {Proceedings of the 2nd ACM conference on Electronic commerce},
  doi = {10.1145/352871.352889},
  interhash = {5c4f092708c065dec499691002d23b22},
  intrahash = {a7bdb5b2f9f8ca80fb87fd8a23850f53},
  isbn = {1-58113-272-7},
  location = {Minneapolis, Minnesota, United States},
  numpages = {8},
  pages = {150--157},
  publisher = {ACM},
  series = {EC '00},
  title = {Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior},
  url = {http://doi.acm.org/10.1145/352871.352889},
  year = 2000
}

@inproceedings{Whitby04filteringout,
  author = {Whitby, Andrew and Jøsang, Audun and Indulska, Jadwiga},
  interhash = {151548725ff496b07dc97c37f28f9f69},
  intrahash = {8d26ba3da23a9ba454281a670bd39a79},
  title = {Filtering Out Unfair Ratings in Bayesian Reputation Systems},
  year = 2004
}

@inproceedings{Jin:2003:CFD:956863.956922,
  abstract = {In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.},
  acmid = {956922},
  address = {New York, NY, USA},
  author = {Jin, Rong and Si, Luo and Zhai, ChengXiang and Callan, Jamie},
  booktitle = {Proceedings of the twelfth international conference on Information and knowledge management},
  doi = {10.1145/956863.956922},
  interhash = {e12feb03e54e75b093319ec861c5dbb5},
  intrahash = {1da2fda40d4a93cb43c7f2f058c0cd3f},
  isbn = {1-58113-723-0},
  location = {New Orleans, LA, USA},
  numpages = {8},
  pages = {309--316},
  publisher = {ACM},
  series = {CIKM '03},
  title = {Collaborative filtering with decoupled models for preferences and ratings},
  url = {http://doi.acm.org/10.1145/956863.956922},
  year = 2003
}

@inproceedings{Claypool:2001:III:359784.359836,
  acmid = {359836},
  address = {New York, NY, USA},
  author = {Claypool, Mark and Le, Phong and Wased, Makoto and Brown, David},
  booktitle = {Proceedings of the 6th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/359784.359836},
  interhash = {1bcb8b08d1ec2e58666acae3d704710a},
  intrahash = {5965344f7121845f069848384fb3e0b9},
  isbn = {1-58113-325-1},
  location = {Santa Fe, New Mexico, United States},
  numpages = {8},
  pages = {33--40},
  publisher = {ACM},
  series = {IUI '01},
  title = {Implicit interest indicators},
  url = {http://doi.acm.org/10.1145/359784.359836},
  year = 2001
}

@inproceedings{Sherchan:2006:FMR:1141277.1141722,
  acmid = {1141722},
  address = {New York, NY, USA},
  author = {Sherchan, Wanita and Loke, Seng W. and Krishnaswamy, Shonali},
  booktitle = {Proceedings of the 2006 ACM symposium on Applied computing},
  doi = {http://doi.acm.org/10.1145/1141277.1141722},
  interhash = {6a847312028872dfb07d2472b4e88ca6},
  intrahash = {afa80aad5c9222e20177793dfae5945a},
  isbn = {1-59593-108-2},
  location = {Dijon, France},
  numpages = {7},
  pages = {1886--1892},
  publisher = {ACM},
  series = {SAC '06},
  title = {A fuzzy model for reasoning about reputation in web services},
  url = {http://doi.acm.org/10.1145/1141277.1141722},
  year = 2006
}

@inproceedings{guha2004rating,
  abstract = {In the offline world, we look to the people we trust and those they trust for reliable information. In this paper, we present a computational model of this phenomenon and show how it can be used to identify high quality content in an Open Rating System, i.e., a system in which any user can rate content. We present a case study (Epinions.com) of a system based on this model and describe a new platform called PeopleNet for harnessing this phenomenon in an open distributed fashion.},
  author = {Guha, R},
  booktitle = {1st Workshop on Friend of a Friend, Social Networking and the Semantic Web},
  interhash = {5c7e0fa8ee4d5a1e204a7153e346e37e},
  intrahash = {57ae21fc256d225e99dff9b74ea1e243},
  month = sep,
  title = {Open rating systems},
  url = {http://www.w3.org/2001/sw/Europe/events/foaf-galway/papers/fp/open_rating_systems/wot.pdf},
  year = 2004
}

@inproceedings{ls_leimeister,
  address = {St. Louis, MO, USA},
  author = {Riedl, C. and Blohm, I. and Leimeister, J. M. and Krcmar, H.},
  booktitle = {30. First International Conference on Information Systems (ICIS) 2010},
  interhash = {07418ee3758f0a35c9d9e2c6c629a057},
  intrahash = {2ad4511439d17f4a38eb8b6515b2abe7},
  note = {203 (51-10)},
  number = 31,
  title = {Rating Scales for Collective Intelligence in Innovation Communities: Why Quick and Easy Decision Making Does Not Get it Right},
  url = {http://pubs.wi-kassel.de/wp-content/uploads/2013/03/JML_195.pdf},
  year = 2010
}