BibSonomy :: publication :: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

Gediminas Adomavicius, and Alexander Tuzhilin. IEEE Trans. on Knowl. and Data Eng. (June 2005)

@article{adomavicius2005toward,
  abstract = {This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.},
  acmid = {1070751},
  added-at = {2011-12-07T08:13:35.000+0100},
  address = {Piscataway, NJ, USA},
  author = {Adomavicius, Gediminas and Tuzhilin, Alexander},
  biburl = {http://www.bibsonomy.org/bibtex/22a99880abc66df6f6997a4b6d4017c4a/beate},
  description = {Toward the Next Generation of Recommender Systems},
  doi = {10.1109/TKDE.2005.99},
  interhash = {42f7653127a823354d000ea95cf804be},
  intrahash = {2a99880abc66df6f6997a4b6d4017c4a},
  issn = {1041-4347},
  issue = {6},
  journal = {IEEE Trans. on Knowl. and Data Eng.},
  keywords = {recommender survey},
  month = {June},
  numpages = {16},
  pages = {734--749},
  publisher = {IEEE Educational Activities Department},
  timestamp = {2011-12-07T08:13:35.000+0100},
  title = {Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions},
  url = {http://dx.doi.org/10.1109/TKDE.2005.99},
  volume = 17,
  year = 2005
}

%0 Journal Article
%1 adomavicius2005toward
%A Adomavicius, Gediminas
%A Tuzhilin, Alexander
%C Piscataway, NJ, USA
%D 2005
%I IEEE Educational Activities Department
%J IEEE Trans. on Knowl. and Data Eng.
%K 
%P 734--749
%T Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions
%R 10.1109/TKDE.2005.99
%U http://dx.doi.org/10.1109/TKDE.2005.99
%V 17
%X This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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DOI:	10.1109/TKDE.2005.99
URL:	http://dx.doi.org/10.1109/TKDE.2005.99
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BibTeX key:	adomavicius2005toward

abstract

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the Next Generation of Recommender Systems

publication post of beate

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

discussion

resources (URL, PDF, ...)

abstract

Description