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Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

by: Yolanda Blanco-Fernández, José J. Pazos-Arias, Alberto Gil-Solla, Manuel Ramos-Cabrer, Martín López-Nores, Jorge García-Duque, Ana Fernández-Vilas, and Rebeca P. Díaz-Redondo

In: Journal of Systems and Software, Vol. 81, Nr. 12 (2008) , p. 2371 - 2385.

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URL:	http://www.sciencedirect.com/science/article/B6V0N-4SHVSS0-3/2/a8dbaf1fad82498263e28296ba0dafdb
DOI:	10.1016/j.jss.2008.05.009

Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.

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ScienceDirect - Journal of Systems and Software : Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

BibTeX record

@article{BlancoFernández20082371,
  abstract = {Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.},
  added-at = {2010-03-15T17:03:55.000+0100},
  author = {Blanco-Fernández, Yolanda and Pazos-Arias, José J. and Gil-Solla, Alberto and Ramos-Cabrer, Manuel and López-Nores, Martín and García-Duque, Jorge and Fernández-Vilas, Ana and Díaz-Redondo, Rebeca P.},
  biburl = {http://www.bibsonomy.org/bibtex/2db354392df86d6e1083350b8385d2384/datentaste},
  description = {ScienceDirect - Journal of Systems and Software : Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study},
  doi = {DOI: 10.1016/j.jss.2008.05.009},
  interhash = {56daf361a2a7004ac213df41fc3f0000},
  intrahash = {db354392df86d6e1083350b8385d2384},
  issn = {0164-1212},
  journal = {Journal of Systems and Software},
  keywords = {2Read Collaborative Content-based Personalization Semantic filtering hpi_ism10},
  note = {Best papers from the 2007 Australian Software Engineering Conference (ASWEC 2007), Melbourne, Australia, April 10-13, 2007, Australian Software Engineering Conference 2007},
  number = 12,
  pages = {2371 - 2385},
  timestamp = {2010-03-15T17:03:55.000+0100},
  title = {Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study},
  url = {http://www.sciencedirect.com/science/article/B6V0N-4SHVSS0-3/2/a8dbaf1fad82498263e28296ba0dafdb},
  volume = 81,
  year = 2008
}

Endnote record

%0 Journal Article
%1 BlancoFernández20082371
%A Blanco-Fernández, Yolanda
%A Pazos-Arias, José J.
%A Gil-Solla, Alberto
%A Ramos-Cabrer, Manuel
%A López-Nores, Martín
%A García-Duque, Jorge
%A Fernández-Vilas, Ana
%A Díaz-Redondo, Rebeca P.
%D 2008
%J Journal of Systems and Software
%K 
%N 12
%P 2371 - 2385
%T Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study
%R DOI: 10.1016/j.jss.2008.05.009
%U http://www.sciencedirect.com/science/article/B6V0N-4SHVSS0-3/2/a8dbaf1fad82498263e28296ba0dafdb
%V 81
%X Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.