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International Conference of the Cross-Language Evaluation Forum for European Languages

CLEF 2015: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp 142–153Cite as

Enhancing Medical Information Retrieval by Exploiting a Content-Based Recommender Method

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9283)

Abstract

Information Retrieval (IR) systems seek to find information which is relevant to a searcher’s information needs. Improving IR effectiveness using personalization has been a significant focus of research attention in recent years. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. This is a particular problem for medical IR where individuals find themselves needing information on topics for which they have never previously searched. However, in all likelihood other users will have searched with the same information need previously. This presents an opportunity to IR researchers attempting to improve search effectiveness by exploiting previous user search behaviour. We describe a method to enhance IR in the medical domain based on recommender systems (RSs) by using a content-based recommender model in combination with a standard IR model. We use search behaviour data from previous users with similar interests to aid the current user to discover better search results. We demonstrate the effectiveness of this method using a test dataset collected as part of the EU FP7 Khresmoi project.

Keywords

  • Information retrieval
  • Content-based filtering
  • Medical search

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References

  1. Beckers, T., Dungs, S., Fuhr, N., Jordan, M., Kriewel, S.: ezDL: an interactive search and evaluation system. In: Proceedings of SIGIR 2012 Workshop on Open-Source Information Retrieval (OSIR 2012), August 2012

    Google Scholar 

  2. Bellogín, A., Wang, J., Castells, P.: Text retrieval methods for item ranking in collaborative filtering. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 301–306. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  3. Costa, A., Roda, F.: Recommender systems by means of information retrieval. In: Proceedings of the International Conference on Web Intelligence, Mining and Semantics (WIMS 2011), Sogndal, Norway, Article No. 57 (2011)

    Google Scholar 

  4. Jansen, B.J., Spink, A., Bateman, J., Saracevic, T.: Real Life Information Retrieval: A Study of User Queries on the Web. ACM SIGIR Forum 32(1), 5–17 (1998)

    CrossRef  Google Scholar 

  5. Jeon, H., Kim, T., Choi, J.: Adaptive user profiling for personalized information retrieval. In: Proceedings of 3rd International Conference on Convergence and Hybrid Information Technology (ICCIT 2008), vol. 2, pp. 836–841. Busan (2008)

    Google Scholar 

  6. Li, W., Jones, G.J.F.: Utilizing recommender algorithms for enhanced information retrieval. In: Proceedings of OAIR 2013 - 10th International Conference in the RIAO series, Lisbon, Portugal (2013)

    Google Scholar 

  7. Mylonas, P., Vallet, D., Castells, P., FernÁndez, M., Avrithis, Y.: Personalized Information Retrieval Based on Context and Ontological Knowledge. Journal of The Knowledge Engineering Review 23(1), 73–100 (2008)

    Google Scholar 

  8. Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    CrossRef  Google Scholar 

  9. Radev, D.R., Jing, H.Y., Styś, M., Tam, D.: Centroid-based summarization of multiple documents. In: Proceeding of Emerging Trends in Engineering and Technology, ICETET 2008 (2008)

    Google Scholar 

  10. Robertson, S.E., Walker, S., Jones, M.S.: Okapi at TREC-3. In: The Proceeding of Second Text Retrieval Conference (TREC-3) (1995)

    Google Scholar 

  11. Smyth, B., Freyne, J., Coyle, M., Briggs, P., Balfe, E.: I-SPY – anonymous, community-based personalization by collaborative meta-search. In: Proceedings of the 23rd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (2003)

    Google Scholar 

  12. Yu, J., Liu, F.F., Zhao, H.H.: Building user profile based on concept and relation for web personalized services. In: International Proceedings of Computer Science & Information Tech., vol, 36, pp. 172 (2012)

    Google Scholar 

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Authors and Affiliations

  1. ADAPT Centre, School of Computing, Dublin City University, Dublin 9, Ireland

    Wei Li & Gareth J. F. Jones

Authors
  1. Wei Li
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  2. Gareth J. F. Jones
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Corresponding authors

Correspondence to Wei Li or Gareth J. F. Jones .

Editor information

Editors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse, Toulouse , France

    Josanne Mothe

  2. Department of Computer Science, University of Neuchatel, Neuchâtel, Switzerland

    Jacques Savoy

  3. Faculteit der Geesteswetenschappen, Universiteit Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  4. Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Karen Pinel-Sauvagnat

  5. School of Computing, Dublin City University, Dublin, Ireland

    Gareth Jones

  6. LIA - CERI, Université d'Avignon et des Pays de Vaucluse, Avignon, France

    Eric San Juan

  7. Department of Information Engineering, University of Padua, Padua, Italy

    Linda Capellato

  8. of Information Engineering (DEI), University of Padua, Department, Padova, Italy

    Nicola Ferro

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Li, W., Jones, G.J.F. (2015). Enhancing Medical Information Retrieval by Exploiting a Content-Based Recommender Method. In: , et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-24027-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24026-8

  • Online ISBN: 978-3-319-24027-5

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