SWAP - Semantic Web Access and Personalization Research Group

Membri.FedelucioNarducci History

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  • AUGUST 2014 - I got a post-doc at University of Bari under the supervision of Dr. Marco de Gemmis. My research was focused on techniques for the information filtering oriented to diversify multimedia recommendations.
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  • AUGUST 2014 - I got a post-doc at University of Bari under the supervision of Dr. Marco de Gemmis. My research was focused on techniques of information filtering for diversifying recommendations of multimedia content.
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  • APRIL 2011 - I joined the Human Interaction and Experiences research group of the Philips Research center in Eindhoven (The Netherlands) for a three months internship under the supervision of Ing. Mauro Barbieri.
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  • APRIL 2011 - I joined the Human Interaction and Experiences research group of the Philips Research center in Eindhoven (The Netherlands) for a three months fellowship under the supervision of Ing. Mauro Barbieri.
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  • AUGUST 2014 - I got a post-doc at University of Bari under the supervision of Dott. Marco de Gemmis. My research was focused on techniques for the information filtering oriented to diversify multimedia recommendations.
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  • AUGUST 2014 - I got a post-doc at University of Bari under the supervision of Dr. Marco de Gemmis. My research was focused on techniques for the information filtering oriented to diversify multimedia recommendations.
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  • JUNE 2012 - I received a Ph.D. in Computer Science from University of Bari. I discussed a thesis titled: ″`Knowledge-enriched Representations for Content-based Recommender Systems″
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  • JUNE 2012 - I received a Ph.D. in Computer Science from University of Bari under the supervision of Dr. Pasquale Lops. I discussed a thesis titled: ″`Knowledge-enriched Representations for Content-based Recommender Systems″
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Research Assistant, PhD

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Postdoctoral Researcher, PhD

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Intelligent Information Access

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  • Hybrid Recommendation Strategies
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Personalization

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  • Semantic Web Personalization

Web 2.0

  • Recommender Systems for Web 2.0
  • Tag Recommendation
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  • Linked Open Data
  • e-Government
  • e-Health
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  • AUGUST 2014 - I got a post-doc at University of Bari under the supervision of Dott. Marco de Gemmis. My research was focused on techniques for the information filtering oriented to diversify multimedia recommendations.
  • APRIL 2012 - July 2014 I got a post-doc at University of Milano Bicocca under the supervision of Prof. Carlo Batini. My research was focused on techniques for the intelligent access to multilingual e-gov service.
  • JUNE 2012 - I received a Ph.D. in Computer Science from University of Bari. I discussed a thesis titled: ″`Knowledge-enriched Representations for Content-based Recommender Systems″
  • APRIL 2011 - I joined the Human Interaction and Experiences research group of the Philips Research center in Eindhoven (The Netherlands) for a three months internship under the supervision of Ing. Mauro Barbieri.
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  • 1st Place - Top-N recommendation from binary user feedback - ESWC-14 Challenge Linked Open Data-enabled Recommender Systems: P. Basile, C. Musto, M. De Gemmis, P. Lops, F. Narducci and G. Semeraro. Aggregation strategies for Linked Open Data-enabled Recommender Systems, 2014.
  • Most Inspiring Contribution Award - 21st Conference on User Modeling, Adaptation and Personalization. F. Narducci, C. Musto, G. Semeraro, P. Lops, M. De Gemmis. Leveraging Encyclopedic Knowledge for Transparent and Serendipitous, 2013 User Pro les.
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Publications

Chapters in International Volumes

  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. Learning Preference Models in Recommender Systems. In Preference Learning. Springer, 2009.
  • P. Lops, M. de Gemmis, G. Semeraro, C. Musto, and F. Narducci, and M. Bux. Semantic Content-based Recommender System Integrating Folksonomies for Personalized Access. In Web Personalization in Intelligent Environment. Springer, 2009.

Conference Papers

  • P. Lops, M. de Gemmis, G. Semeraro, F. Narducci, C. Musto. Leveraging the linkedin social network data for extracting content-based user profiles. In Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, October 23-27, 2011, ISBN 978-1-4503-0683-6.
  • C. Musto, F. Narducci, P. Basile, P. Lops, M. de Gemmis, and G. Semeraro. Cross-Language Information Filtering: Word Sense Disambiguation vs. Distributional Models. In Proceedings of AI*IA 2011: Artificial Intelligence Around Man and Beyon - XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011, ISBN 978-3-642-03963-8.
  • C. Musto, F. Narducci, P. Lops, M. de Gemmis, G. Semeraro. Exploiting content-based recommender systems and digital libraries for cultural heritage personalization. In Proceedings of the 6th Italian Research Conference on Digital Libraries - IRCDL 2010, Padua, Italy, January 28-29, 2010.
  • P. Lops, C. Musto, F. Narducci, M. de Gemmis, P. Basile and G. Semeraro. Cross-Language Personalization through a Semantic Content-Based Recommender System. In Proceedings of Artificial Intelligence: Methodology, Systems, and Applications, 14th International Conference, AIMSA 2010, Varna, Bulgaria, September 8-10, 2010, volume 6304 of Lecture Notes in Computer Science, ISBN 978-3-642-15430-0.
  • C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro. Content-based Personalization Services Integrating Folksonomies. In E-Commerce and Web Technologies, 10th International Conference, EC-Web 2009, Linz, Austria, September 1-4, 2009, volume 5692 of Lecture Notes in Computer Science. 217-228, BERLIN: Springer, 2009, ISBN 978-3-642-03963-8
  • P. Basile, M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. SpIteR : a Module for Recommending Dynamic Personalized Museum Tours In Proceedings of the 2009 IEEE/WIC/ACM Int’l Joint Conf. on Web Intelligence & Intelligent Agent Technology (WI-IAT 2009) LOS ALAMITOS, CA – USA: IEEE Computer Society Press.
  • P. Lops, M. de Gemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci. Content-based Filtering with Tags : the FIRSt System. In Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA 2009).
  • C. Musto, F. Narducci, M. de Gemmis, P. Lops, G. Semeraro. A Tag Recommender System Exploiting User and Community Behavior. In Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems & The Social Web. NEW YORK: Association for Computing Machinery, Inc. (ACM). To appear.

National and International Workshops

  • F. Narducci, G. Semeraro, P. Lops, M. De Gemmis. Explicit Semantic Analysis for Enriching Content-Based User Profiles. In Proceedings of the 2nd Italian Information Retrieval (IIR) Workshop, Milan, Italy, January 27-28, 2011. Edited by: Massimo Melucci, Stefano Mizzaro, Gabriella Pasi. CEUR-WS.org, Volume 704 of CEUR Workshop Proceedings.
  • C. Musto, F. Narducci, M. De Gemmis, P. Lops, G. Semeraro. An IR-based approach for Tag Recommendation. In Proceedings of the First Italian Information Retrieval Workshop (IIR-2010), Padua, Italy, January 27-28, 2010. Edited by: Massimo Melucci, Stefano Mizzaro, Gabriella Pasi. CEUR-WS.org, Volume 560 of CEUR Workshop Proceedings.
  • C. Musto, F. Narducci, M. de Gemmis, P. Lops, G. Semeraro. STaR : a Social Tag Recommender System. In Proceedings of the ECML/PKDD 2009 Discovery Challenge Workshop. CEUR-WS.org, Volume 497 of CEUR Workshop Proceedings, ISSN 1613-0073.
  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, and G. Semeraro. Preference Learning in Recommender Systems. In Proceedings of the ECML/PKDD 2009 Workshop on Preference Learning. To appear.
  • P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. Augmenting a Content-based Recommender System with Tags for Cultural Heritage Personalization. In L. Aroyo, T. Kuflik, O. Stock, and M. Zancanaro, editors, Proceedings of the Workshop on Personalized Access to Cultural Heritage (PATCH 2008) at the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008), Hannover, Germany, pages 25-34. July 29, 2008.
  • P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. Cultural Heritage Personalization using a Content-based Recommender System and Folksonomies. In G. Armano, M. Schaerf, and G. Semeraro, editors, Proceedings of the AI*IA 2008 Workshop on Artificial Intelligence for the Cultural Heritage, Cagliari, Italy, pages 46-54. September 11, 2008.
  • P. Basile, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. First: a content-based recommender system integrating tags for cultural heritage personalization. In Proceedings of the 4th International Conference on Automated Solutions for Cross Media Content and Multi-channel Distribution (AXMEDIS 2008) - Workshop on Cultural heritage and Artificial Intelligence, Florence, Italy, 17-19 November, 2008.



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  • MovieRecSysBot. Conversational Movie Recommender implemented as Telegram chatbot
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Ph.D. Student

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Research Assistant, PhD

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  • SSSC 2010 - IEEE 2010 Summer School on Semantic Computing - Berkeley (CA, United States), July 25-31, 2010 - 1st prize award (link) for the contribution in the Student Project works organized within the Summer School. The work focused on the development of GeoMusic a tool for the suggestion of personalized and geo-localized music playlists.
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  • SSSC 2010 - IEEE 2010 Summer School on Semantic Computing - Berkeley (CA, United States), July 25-31, 2010 - 1st prize award for the contribution in the Student Project works organized within the Summer School. The work focused on the development of GeoMusic a tool for the suggestion of personalized and geo-localized music playlists.
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  • ITR. ITem Recommender is a content-based item recommending on the basis of ratings given by users. It use a Naïve Bayes text classification to assign a score (level of interest) to items according to the user preferences. The user profile containing the probabilistic model (words/synsets + probabilities) of user preferences.
  • STAR. Social Tag Recommender is a content-based tag recommender. It suggests tags for Bibsonomy bookmarks and BibTeXs entries exploiting tags used by the community to annotate similar resources. STaR participates at the ECML/PKDD Discovery Challenge 2009.
to:
  • FIRSt. Folksonomy-based ITem Recommender is a content-based item recommending on the basis of ratings given by users. It use a Naïve Bayes text classification to assign a score (level of interest) to items according to the user preferences. The user profile containing the probabilistic model (words/synsets + probabilities) of user preferences.
  • STAR. Social Tag Recommender is a content-based tag recommender. It suggests tags for Bibsonomy bookmarks and BibTeXs entries exploiting tags used by the community to annotate similar resources. STaR participates at the ECML/PKDD Discovery Challenge 2009.
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  • APRIL 2008 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Parameters estimating and feature weighting in a bayesian classifier: applying of Poisson's distribution and risk assessment ″.

Summer Schools

  • ACAI 2009 - Advanced Course in Artificial Intelligence '09 Intelligent Decision Support Systems (theory, algorithms, and applications) - Belfast, August 23-29, 2009
to:
  • APRIL 2008 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Parameters estimating and feature weighing in a bayesian classifier: applying of Poisson's distribution and risk assessment ″.

Summer Schools

  • SSSC 2010 - IEEE 2010 Summer School on Semantic Computing - Berkeley (CA, United States), July 25-31, 2010
  • ACAI 2009 - Advanced Course in Artificial Intelligence '09 Intelligent Decision Support Systems (theory, algorithms, and applications) - Belfast (Northern Ireland), August 23-29, 2009

Awards

  • SSSC 2010 - IEEE 2010 Summer School on Semantic Computing - Berkeley (CA, United States), July 25-31, 2010 - 1st prize award (link) for the contribution in the Student Project works organized within the Summer School. The work focused on the development of GeoMusic a tool for the suggestion of personalized and geo-localized music playlists.

Tutoring

  • 2009/10 - Corso di Programmazione, CdL in Informatica e Tecnologie per la produzione del software.
  • 2009/10 - Corso di Linguaggi di Programmazione + Lab. - CdL in Informatica - (Link) - (Join Facebook Group)
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  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. Learning Preference Models in Recommender Systems. In Preference Learning. Springer, 2009. To appear.
  • P. Lops, M. de Gemmis, G. Semeraro, C. Musto, and F. Narducci, and M. Bux. Semantic Content-based Recommender System Integrating Folksonomies for Personalized Access. In Web Personalization in Intelligent Environment. Springer, 2009. To appear.
to:
  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. Learning Preference Models in Recommender Systems. In Preference Learning. Springer, 2009.
  • P. Lops, M. de Gemmis, G. Semeraro, C. Musto, and F. Narducci, and M. Bux. Semantic Content-based Recommender System Integrating Folksonomies for Personalized Access. In Web Personalization in Intelligent Environment. Springer, 2009.
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  • P. Lops, M. de Gemmis, G. Semeraro, F. Narducci, C. Musto. Leveraging the linkedin social network data for extracting content-based user profiles. In Proceedings of the 2011 ACM Conference on Recommender Systems, RecSys 2011, Chicago, IL, USA, October 23-27, 2011, ISBN 978-1-4503-0683-6.
  • C. Musto, F. Narducci, P. Basile, P. Lops, M. de Gemmis, and G. Semeraro. Cross-Language Information Filtering: Word Sense Disambiguation vs. Distributional Models. In Proceedings of AI*IA 2011: Artificial Intelligence Around Man and Beyon - XIIth International Conference of the Italian Association for Artificial Intelligence, Palermo, Italy, September 15-17, 2011, ISBN 978-3-642-03963-8.
  • C. Musto, F. Narducci, P. Lops, M. de Gemmis, G. Semeraro. Exploiting content-based recommender systems and digital libraries for cultural heritage personalization. In Proceedings of the 6th Italian Research Conference on Digital Libraries - IRCDL 2010, Padua, Italy, January 28-29, 2010.
  • P. Lops, C. Musto, F. Narducci, M. de Gemmis, P. Basile and G. Semeraro. Cross-Language Personalization through a Semantic Content-Based Recommender System. In Proceedings of Artificial Intelligence: Methodology, Systems, and Applications, 14th International Conference, AIMSA 2010, Varna, Bulgaria, September 8-10, 2010, volume 6304 of Lecture Notes in Computer Science, ISBN 978-3-642-15430-0.
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  • P. Basile, M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. SpIteR : a Module for Recommending Dynamic Personalized Museum Tours In Proceedings of the 2009 IEEE/WIC/ACM Int’l Joint Conf. on Web Intelligence & Intelligent Agent Technology (WI-IAT 2009) LOS ALAMITOS, CA – USA: IEEE Computer Society Press. To appear.
  • P. Lops, M. de Gemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci. Content-based Filtering with Tags : the FIRSt System. In Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA 2009). To appear.
to:
  • P. Basile, M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. SpIteR : a Module for Recommending Dynamic Personalized Museum Tours In Proceedings of the 2009 IEEE/WIC/ACM Int’l Joint Conf. on Web Intelligence & Intelligent Agent Technology (WI-IAT 2009) LOS ALAMITOS, CA – USA: IEEE Computer Society Press.
  • P. Lops, M. de Gemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci. Content-based Filtering with Tags : the FIRSt System. In Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA 2009).
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  • F. Narducci, G. Semeraro, P. Lops, M. De Gemmis. Explicit Semantic Analysis for Enriching Content-Based User Profiles. In Proceedings of the 2nd Italian Information Retrieval (IIR) Workshop, Milan, Italy, January 27-28, 2011. Edited by: Massimo Melucci, Stefano Mizzaro, Gabriella Pasi. CEUR-WS.org, Volume 704 of CEUR Workshop Proceedings.
  • C. Musto, F. Narducci, M. De Gemmis, P. Lops, G. Semeraro. An IR-based approach for Tag Recommendation. In Proceedings of the First Italian Information Retrieval Workshop (IIR-2010), Padua, Italy, January 27-28, 2010. Edited by: Massimo Melucci, Stefano Mizzaro, Gabriella Pasi. CEUR-WS.org, Volume 560 of CEUR Workshop Proceedings.
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  • APRIL 2008 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Parameters estimation and feature weighting in a bayesian classifier: applying of Poisson's distribution and risk assessment ″.
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  • APRIL 2008 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Parameters estimating and feature weighting in a bayesian classifier: applying of Poisson's distribution and risk assessment ″.
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http://profile.ak.fbcdn.net/v229/429/30/n1625528838_5242.jpg Fedelucio Narducci
Ph.D. Student

University of Bari "Aldo Moro"
Department of Computer Science
Via E.Orabona, 4 - 70126 BARI, Italy

Phone: +39 080 5442298
Fax: +39 080 5443196
e-mail: narducci___AT___di.uniba.it






Research Interests and Activities | Short Curriculum | Publications | Tools | Links





Main Research Interests

Intelligent Information Access

  • Recommender Systems
  • Information Filtering
  • Hybrid Recommendation Strategies
  • Machine Learning Techniques for Recommender Systems

Personalization

  • User Modeling and Profiling
  • Semantic Web Personalization

Web 2.0

  • Recommender Systems for Web 2.0
  • Tag Recommendation



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Short CV

  • JANUARY 2009 - I started the Ph.D. Course in Computer Science at Department of Computer Science, University of Bari. My supervisor is PhD Pasquale Lops.
  • APRIL 2008 - I received a Master's Degree with full marks and honors in Computer Science from University of Bari. I discussed a thesis titled: ″Parameters estimation and feature weighting in a bayesian classifier: applying of Poisson's distribution and risk assessment ″.

Summer Schools

  • ACAI 2009 - Advanced Course in Artificial Intelligence '09 Intelligent Decision Support Systems (theory, algorithms, and applications) - Belfast, August 23-29, 2009



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Associations

  • AI*IA (Associazione Italiana per l'Intelligenza Artificiale);



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Publications

Chapters in International Volumes

  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. Learning Preference Models in Recommender Systems. In Preference Learning. Springer, 2009. To appear.
  • P. Lops, M. de Gemmis, G. Semeraro, C. Musto, and F. Narducci, and M. Bux. Semantic Content-based Recommender System Integrating Folksonomies for Personalized Access. In Web Personalization in Intelligent Environment. Springer, 2009. To appear.

Conference Papers

  • C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro. Content-based Personalization Services Integrating Folksonomies. In E-Commerce and Web Technologies, 10th International Conference, EC-Web 2009, Linz, Austria, September 1-4, 2009, volume 5692 of Lecture Notes in Computer Science. 217-228, BERLIN: Springer, 2009, ISBN 978-3-642-03963-8
  • P. Basile, M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, G. Semeraro. SpIteR : a Module for Recommending Dynamic Personalized Museum Tours In Proceedings of the 2009 IEEE/WIC/ACM Int’l Joint Conf. on Web Intelligence & Intelligent Agent Technology (WI-IAT 2009) LOS ALAMITOS, CA – USA: IEEE Computer Society Press. To appear.
  • P. Lops, M. de Gemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci. Content-based Filtering with Tags : the FIRSt System. In Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA 2009). To appear.
  • C. Musto, F. Narducci, M. de Gemmis, P. Lops, G. Semeraro. A Tag Recommender System Exploiting User and Community Behavior. In Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems & The Social Web. NEW YORK: Association for Computing Machinery, Inc. (ACM). To appear.

National and International Workshops

  • C. Musto, F. Narducci, M. de Gemmis, P. Lops, G. Semeraro. STaR : a Social Tag Recommender System. In Proceedings of the ECML/PKDD 2009 Discovery Challenge Workshop. CEUR-WS.org, Volume 497 of CEUR Workshop Proceedings, ISSN 1613-0073.
  • M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, and G. Semeraro. Preference Learning in Recommender Systems. In Proceedings of the ECML/PKDD 2009 Workshop on Preference Learning. To appear.
  • P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. Augmenting a Content-based Recommender System with Tags for Cultural Heritage Personalization. In L. Aroyo, T. Kuflik, O. Stock, and M. Zancanaro, editors, Proceedings of the Workshop on Personalized Access to Cultural Heritage (PATCH 2008) at the 5th International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 2008), Hannover, Germany, pages 25-34. July 29, 2008.
  • P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. Cultural Heritage Personalization using a Content-based Recommender System and Folksonomies. In G. Armano, M. Schaerf, and G. Semeraro, editors, Proceedings of the AI*IA 2008 Workshop on Artificial Intelligence for the Cultural Heritage, Cagliari, Italy, pages 46-54. September 11, 2008.
  • P. Basile, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, and F. Narducci. First: a content-based recommender system integrating tags for cultural heritage personalization. In Proceedings of the 4th International Conference on Automated Solutions for Cross Media Content and Multi-channel Distribution (AXMEDIS 2008) - Workshop on Cultural heritage and Artificial Intelligence, Florence, Italy, 17-19 November, 2008.



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Tools

  • ITR. ITem Recommender is a content-based item recommending on the basis of ratings given by users. It use a Naïve Bayes text classification to assign a score (level of interest) to items according to the user preferences. The user profile containing the probabilistic model (words/synsets + probabilities) of user preferences.
  • STAR. Social Tag Recommender is a content-based tag recommender. It suggests tags for Bibsonomy bookmarks and BibTeXs entries exploiting tags used by the community to annotate similar resources. STaR participates at the ECML/PKDD Discovery Challenge 2009.



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