Skip to main content

Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 91))

Abstract

Throughout the last decade, the area of Digital Libraries (DL) get more and more interest from both the research and development communities. Likewise, since the release of new platforms enriches them with new features and makes DL more powerful and effective, the number of web sites integrating these kind of tools is rapidly growing. In this paper we propose an approach for the exploitation of digital libraries for personalization goal in cultural heritage scenario. Specifically, we tried to integrate FIRSt (Folksonomy-based Item Recommender syStem), a content-based recommender system developed at the University of Bari, and Fedora, a flexible digital library architecture, in a framework for the adaptive fruition of cultural heritage implemented within the activities of the CHAT research project. In this scenario, the role of the digital library was to store information (such as textual and multimedial ones) about paintings gathered from the Vatican Picture Gallery and to provide them in a multimodal and personalized way through a PDA device given to a user before her visit in a museum. This paper describes the system architecture of our recommender system and its integration in the framework implemented for the CHAT project, showing how this recommendation model has been applied to recommend the artworks located at the Vatican Picture Gallery (Pinacoteca Vaticana), providing users with a personalized museum tour tailored on their tastes. The experimental evaluation we performed also confirmed that these recommendation services are really able to catch the real user preferences thus improving their experience in cultural heritage fruition.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balabanovic, M., Shoham, Y.: Fab: Content-based, Collaborative Recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  2. Basile, P., de Gemmis, M., Gentile, A.L., Iaquinta, L., Lops, P., Semeraro, G.: META - MultilanguagE Text Analyzer. In: Proceedings of the Language and Speech Technnology Conference - LangTech 2008, Rome, Italy, February 28-29, pp. 137–140 (2008)

    Google Scholar 

  3. Basile, P., de Gemmis, M., Lops, P., Semeraro, G., Bux, M., Musto, C., Narducci, F.: FIRSt: a Content-based Recommender System Integrating Tags for Cultural Heritage Personalization. In: Nesi, P., Ng, K., Delgado, J. (eds.) Proceedings of the 4th International Conference on Automated Solutions for Cross Media Content and Multi-channel Distribution (AXMEDIS 2008) - Workshop Panels and Industrial Applications, Florence, Italy, November 17-19, pp. 103–106. Firenze University Press (2008)

    Google Scholar 

  4. Degemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating Tags in a Semantic Content-based Recommender. In: Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys 2008, Lausanne, Switzerland, October 23-25, pp. 163–170 (2008)

    Google Scholar 

  5. Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing 7(1), 76–80 (2003)

    Article  Google Scholar 

  6. Lops, P., Degemmis, M., Semeraro, G.: Improving Social Filtering Techniques Through WordNet-Based User Profiles. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 268–277. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Miller, G.: WordNet: An On-Line Lexical Database. International Journal of Lexicography 3(4) (1990) (Special Issue)

    Google Scholar 

  8. Mladenic, D.: Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems 14(4), 44–54 (1999)

    Article  Google Scholar 

  9. Resnick, P., Varian, H.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  10. Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1) (2002)

    Google Scholar 

  11. Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining Learning and Word Sense Disambiguation for Intelligent User Profiling. In: Veloso, M.M. (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2856–2861 (2007) ISBN 978-I-57735-298-3

    Google Scholar 

  12. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings of ACM CHI 1995 Conference on Human Factors in Computing Systems, vol. 1, pp. 210–217 (1995)

    Google Scholar 

  13. Stock, O., Zancanaro, M., Busetta, P., Callaway, C.B., Krüger, A., Kruppa, M., Kuflik, T., Not, E., Rocchi, C.: Adaptive, intelligent presentation of information for the museum visitor in peach. User Model. User-Adapt. Interact. 17(3), 257–304 (2007)

    Article  Google Scholar 

  14. Trant, J., Wyman, B.: Investigating social tagging and folksonomy in art museums with steve.museum. In: Collaborative Web Tagging Workshop at WWW 2006, Edinburgh, Scotland (May 2006)

    Google Scholar 

  15. Wang, Y., Aroyo, L., Stash, N., Rutledge, L.: Interactive user modeling for personalized access to museum collections: The rijksmuseum case study. In: User Modeling, 385–389 (2007)

    Google Scholar 

  16. Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. Journal of the American Society for Information Science 46(2), 133–145 (1995)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Musto, C., Narducci, F., Lops, P., de Gemmis, M., Semeraro, G. (2010). Integrating a Content-Based Recommender System into Digital Libraries for Cultural Heritage. In: Agosti, M., Esposito, F., Thanos, C. (eds) Digital Libraries. IRCDL 2010. Communications in Computer and Information Science, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15850-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15850-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15849-0

  • Online ISBN: 978-3-642-15850-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics