BibSonomy :: publication :: Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Bahram Amini, Roliana Ibrahim, and Mohd Shahizan Othman. (2011)cite arxiv:1109.0166Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey IJCSES Vol.2, No.3, August 2011.

@misc{amini2011discovering,
  abstract = {Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.},
  added-at = {2012-07-22T12:15:19.000+0200},
  author = {Amini, Bahram and Ibrahim, Roliana and Othman, Mohd Shahizan},
  biburl = {http://www.bibsonomy.org/bibtex/27828683e4351b4a47a06307416e29ce5/iyas_hilal},
  description = {[1109.0166] Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study},
  doi = {10.5121/ijcses.2011.2301},
  interhash = {416a26de386e35fd652cfb9546ee9b27},
  intrahash = {7828683e4351b4a47a06307416e29ce5},
  keywords = {recommender system},
  note = {cite arxiv:1109.0166Comment: 14 pages, 3 tables; International Journal of Computer Science &  Engineering Survey (IJCSES) Vol.2, No.3, August 2011},
  timestamp = {2012-07-22T12:15:19.000+0200},
  title = {Discovering the Impact of Knowledge in Recommender Systems: A
  Comparative Study},
  url = {http://arxiv.org/abs/1109.0166},
  year = 2011
}

%0 Generic
%1 amini2011discovering
%A Amini, Bahram
%A Ibrahim, Roliana
%A Othman, Mohd Shahizan
%D 2011
%K 
%T Discovering the Impact of Knowledge in Recommender Systems: A
  Comparative Study
%R 10.5121/ijcses.2011.2301
%U http://arxiv.org/abs/1109.0166
%X Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.

discussion

(0)

resources (URL, PDF, ...)

DOI:	10.5121/ijcses.2011.2301
URL:	http://arxiv.org/abs/1109.0166
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BibTeX key:	amini2011discovering

abstract

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.

Description

[1109.0166] Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

publication post of iyas_hilal

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

discussion

resources (URL, PDF, ...)

abstract

Description