@article{kautz1997referral,
  acmid = {245123},
  address = {New York, NY, USA},
  author = {Kautz, Henry and Selman, Bart and Shah, Mehul},
  doi = {10.1145/245108.245123},
  interhash = {6995678b936b33eef9ea1396e53a1fc7},
  intrahash = {832d16a8c86e769c7ac9ace5381f757e},
  issn = {0001-0782},
  issue_date = {March 1997},
  journal = {Communications of the ACM},
  month = mar,
  number = 3,
  numpages = {3},
  pages = {63--65},
  publisher = {ACM},
  title = {Referral Web: combining social networks and collaborative filtering},
  url = {http://doi.acm.org/10.1145/245108.245123},
  volume = 40,
  year = 1997
}

@phdthesis{vansetten2005supporting,
  abstract = {The Internet has provided people with the possibility to easily publish and search for information. This resulted in an enormous amount of available information, products and services that also made it a challenge to find that what is really interesting to a person. Finding something really interesting is like searching for the proverbial needle in a haystack. This thesis addresses solutions to support people in finding interesting items by focusing on information systems that automatically learn and adapt their behaviour in order to support their users. The solutions provided in this thesis correspond to the three main processes in personalized information systems: selecting, structuring and presenting items.
},
  address = {Enschede, The Netherlands},
  author = {van Setten, Mark},
  interhash = {59cae57467580d3bdc6bab08d58bf3c0},
  intrahash = {ab934d0100a38cafc7c635d11d9de9d7},
  isbn = {90-75176-89-9},
  issn = {1388-1795},
  month = dec,
  school = {University of Twente},
  title = {Supporting people in finding information : hybrid recommender systems and goal-based structuring},
  url = {http://doc.utwente.nl/50889/1/thesis_van_Setten.pdf},
  year = 2005
}

@article{burke2002hybrid,
  abstract = {Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.},
  address = {Hingham, MA, USA},
  author = {Burke, Robin},
  doi = {10.1023/A:1021240730564},
  interhash = {f40020400b8bc08adca29a987caf25d8},
  intrahash = {460b623792e13b4ec0e990563e57f26c},
  issn = {0924-1868},
  journal = {User Modeling and User-Adapted Interaction},
  month = nov,
  number = 4,
  pages = {331--370},
  publisher = {Kluwer Academic Publishers},
  title = {Hybrid Recommender Systems: Survey and Experiments},
  url = {http://portal.acm.org/citation.cfm?id=586352},
  volume = 12,
  year = 2002
}

@inproceedings{1506255,
  abstract = {In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or
annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The
inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability
of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.},
  address = {New York, NY, USA},
  author = {Wetzker, Robert and Umbrath, Winfried and Said, Alan},
  booktitle = {ESAIR '09: Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval},
  doi = {10.1145/1506250.1506255},
  interhash = {5a4e686feaa38748f7eac2c8a3afe51e},
  intrahash = {c1397d5b1bf9d8305aecbb9133bdce2c},
  isbn = {978-1-60558-430-0},
  location = {Barcelona, Spain},
  pages = {25--29},
  publisher = {ACM},
  title = {A hybrid approach to item recommendation in folksonomies},
  url = {http://portal.acm.org/citation.cfm?id=1506255},
  year = 2009
}