@incollection{marinho2011social,
  abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the  noise  that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.},
  address = {New York},
  author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  doi = {10.1007/978-0-387-85820-3_19},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {708be7b5c269bd3a9d3d2334f858d52d},
  isbn = {978-0-387-85820-3},
  pages = {615--644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  year = 2011
}

@article{milicevic2010social,
  abstract = {Social tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. This paper presents an overview of the field of social tagging systems which can be used for extending the capabilities of recommender systems. Various limitations of the current generation of social tagging systems and possible extensions that can provide better recommendation capabilities are also considered.},
  author = {Milicevic, Aleksandra and Nanopoulos, Alexandros and Ivanovic, Mirjana},
  doi = {10.1007/s10462-009-9153-2},
  interhash = {d9e331d46fe4da88400036ad404c4535},
  intrahash = {efb6cb6220dfdd1e3d9ca4894e9f1459},
  issn = {0269-2821},
  journal = {Artificial Intelligence Review},
  month = jan,
  number = 3,
  pages = {187--209},
  publisher = {Springer Netherlands},
  title = {Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions},
  url = {http://dx.doi.org/10.1007/s10462-009-9153-2},
  volume = 33,
  year = 2010
}

@incollection{marinho2011social,
  abstract = {The new generation of Web applications known as (STS) is successfully established and poised for continued growth. STS are open and inherently social; features that have been proven to encourage participation. But while STS bring new opportunities, they revive old problems, such as information overload. Recommender Systems are well known applications for increasing the level of relevant content over the  noise  that continuously grows as more and more content becomes available online. In STS however, we face new challenges. Users are interested in finding not only content, but also tags and even other users. Moreover, while traditional recommender systems usually operate over 2-way data arrays, STS data is represented as a third-order tensor or a hypergraph with hyperedges denoting (user, resource, tag) triples. In this chapter, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve STS.We describe (a) novel facets of recommenders for STS, such as user, resource, and tag recommenders, (b) new approaches and algorithms for dealing with the ternary nature of STS data, and (c) recommender systems deployed in real world STS. Moreover, a concise comparison between existing works is presented, through which we identify and point out new research directions.},
  address = {New York},
  author = {Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd and Symeonidis, Panagiotis},
  booktitle = {Recommender Systems Handbook},
  doi = {10.1007/978-0-387-85820-3_19},
  editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.},
  interhash = {2d4afa6f7fb103ccc166c9c5d629cdd1},
  intrahash = {708be7b5c269bd3a9d3d2334f858d52d},
  isbn = {978-0-387-85820-3},
  pages = {615--644},
  publisher = {Springer},
  title = {Social Tagging Recommender Systems},
  url = {http://dx.doi.org/10.1007/978-0-387-85820-3_19},
  vgwort = {50},
  year = 2011
}