Skip to main content
Log in

An experimental evaluation of ontology-based user profiles

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent years, a number of research works have been carried out to improve the information retrieval process by exploiting external knowledge, e.g. by employing ontologies. Even though ontologies seem to be a promising technique to improve the retrieval process, hardly any study has been performed to evaluate the use of ontologies over a longer time period to model user interests. In this work we introduce an ontology based video recommender system that exploits implicit relevance feedback to capture users’ evolving information needs. The system exploits a generic ontology to organise users’ interests. We evaluate the recommendations by performing a user-centred multiple time-series study where participants were asked to include the system into their daily news gathering routine. The results of this study suggest that the system can be successfully employed to improve personal information seeking tasks in news domain.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.iptc.org/

  2. http://www.w3.org/TR/skos-reference/

References

  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans on Knowl and Data Eng 17(6):734–749

    Article  Google Scholar 

  2. Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives ZG (2007) DBpedia: a nucleus for a web of open data. In: Proc. 6th int. semantic web conf. Springer, Berlin/Heidelberg, pp 722–735

    Google Scholar 

  3. Belkin NJ (2008) Some(what) grand challenges for information retrieval. SIGIR Forum 42(1):47–54

    Article  Google Scholar 

  4. Bharat K, Kamba T, Albers M (1998) Personalized, interactive news on the web. Mult Syst 6(5):349–358

    Article  Google Scholar 

  5. Borlund P (2003) The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Inf Res 8(3) http://informationr.net/ir/8-3/paper152.html

  6. Campbell DT, Stanley JC (1963) Experimental and quasi-experimental design for research, 1st edn. Wadsworth Publishing, Monterey

    Google Scholar 

  7. Chen L, Sycara K (1998) WebMate: a personal agent for browsing and searching. In: Sycara KP, Wooldridge M (eds) Proc. agents’98, vol 9–13. ACM Press, New York, pp 132–139

    Google Scholar 

  8. Christel MG (2007) Establishing the utility of non-text search for news video retrieval with real world users. In: MULTIMEDIA ’07: proceedings of the 15th international conference on multimedia. ACM, New York, pp 707–716

    Google Scholar 

  9. Christel MG (2007) Examining user interactions with video retrieval systems. In: SPIE’06: proceedings of SPIE, multimedia content access: algorithms and systems, vol 6506

  10. Dudev M, Elbassuoni S, Luxenburger J, Ramanath M, Weikum G (2008) Personalizing the search for knowledge. In: Proc. PersDB

  11. Fernández M, López V, Sabou M, Uren V, Vallet D, Motta E, Castells P (2009) Using TREC for cross-comparison between classic IR and ontology-based search models at a web scale. In: SemSearch’09

  12. Fernández N, Blázquez JM, Fisteus JA, Sánchez L, Sintek M, Bernardi A, Fuentes M, Marrara A, Ben-Asher Z (2006) NEWS: bringing semantic web technologies into news agencies. In: Proc. ISWC, pp 778–791

  13. Hopfgartner F (2011) Adaptive interactive news video recommendation: an example system. SEMAIS’11 - second international workshop on semantic models for adaptive interactive systems, vol 2. Palo Alto, CA, USA, pp 21–25

  14. Hopfgartner F, Jose JM (2010) Semantic user modelling for personal news video retrieval. MMM’10 - 16th international conference on multimedia modeling, vol 1. Chongqing, China, Springer Verlag, pp 336–349

    Google Scholar 

  15. Hopfgartner F, Jose JM (2010) Semantic user profiling techniques for personalised multimedia recommendation. ACM/Springer multimedia systems, vol 16, issue 4. Springer-Verlag New York, Inc., Secaucus, NJ, USA, pp 255–274

  16. Hopfgartner F, Jose JM (2011) Development of a test collection for studying long-term user modelling. In: LWA’11: proceedings of the workshop information retrieval at Lernen, Wissen, Adaptivitaet

  17. Hopfgartner F, Vallet D, Halvey M, Jose JM (2008) Search trails using user feedback to improve video search. In: Proc. of the ACM int. conf. on multimedia, pp 339–348

  18. Kelly D (2004) Understanding implicit feedback and document preference: a naturalistic user study. PhD thesis, Rutgers University

  19. Kelly D, Dumais ST, Pedersen JO (2009) Evaluation challenges and directions for information-seeking support systems. IEEE Comput 42(3):60–66

    Article  Google Scholar 

  20. Liu J, Belkin NJ (2010) Personalizing information retrieval for multi-session tasks: the roles of task stage and task type. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, SIGIR ’10. ACM, New York, pp 26–33

    Google Scholar 

  21. Misra H, Hopfgartner F, Goyal A, Punitha P, Jose JM (2010) News video story segmentation based on semantic coherence and content similarity. MMM’10 - 16th international conference on multimedia modeling, vol 1. Chongqing, China, Springer Verlag, pp 347-357

  22. Pérez-Agüera JR, Arroyo J, Greenberg J, Perez Iglesias J, Fresno V (2010) Using bm25f for semantic search. In: Semantic search 2010 workshop

  23. Ruthven I, Kelly D (2011) Interactive information seeking behaviour and retrieval. Facet Press, Monterey

    Google Scholar 

  24. Shneiderman B, Plaisant C (2006) Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In: BELIV’06, pp 1–7

  25. Vallet D, Hopfgartner F, Jose JM, Castells P (2011) Effects of usage-based feedback on video retrieval: a simulation-based study. ACM Trans Inf Syst 29(2):11:1–11:32

    Article  Google Scholar 

  26. Zaragoza H, Craswell N, Taylor MJ, Saria S, Robertson SE (2004) Microsoft Cambridge at TREC 13: web and hard tracks. In: TREC’04: proceedings of the 13th text retrieval conference

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frank Hopfgartner.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hopfgartner, F., Jose, J.M. An experimental evaluation of ontology-based user profiles. Multimed Tools Appl 73, 1029–1051 (2014). https://doi.org/10.1007/s11042-012-1254-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1254-2

Keywords

Navigation