Multimedia Tools and Applications

, Volume 58, Issue 1, pp 167–213 | Cite as

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

  • Toon De PessemierEmail author
  • Sam Coppens
  • Kristof Geebelen
  • Chris Vleugels
  • Stijn Bannier
  • Erik Mannens
  • Kris Vanhecke
  • Luc Martens


Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.


Recommender system Personalization Event modeling Distributing event information 



The research activities that have been described in this paper were funded by Ghent University, K.U. Leuven, VUB, VRT-medialab, Interdisciplinary Institute for Broadband Technology (IBBT) through the CUPID project (50% co-funded by industrial partners), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders), and the European Union.


  1. 1.
    Beckett D (ed) (2004) RDF/XML syntax specification (revised). W3C recommendation. World Wide Web Consortium. Available at
  2. 2.
    Bizer C, Heath T, Idehen K, Berners-Lee T (2008) Linked data on the web. In: Proceedings of the 17th international world wide web conference—LDOW workshop. Beijing, China, pp 1265–1266Google Scholar
  3. 3.
    Bray T, Paoli J, Sperberg-McQueen C, Maler E, Yergeau F (eds) (2006) Extensible markup language (XML) 1.0, 4th edn. W3C recommendation. World Wide Web Consortium. Available at
  4. 4.
    Breese J, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th conference on uncertainty in artificial intelligence. Madison, USA, pp 43–52Google Scholar
  5. 5.
    Brickley D (ed) (2004) RDF vocabulary description language 1.0: RDF schema. W3C recommendation. World Wide Web Consortium. Available at
  6. 6.
    Campochiaro E, Casatta R, Cremonesi P, Turrin R (2009) Do metrics make recommender algorithms? In: Advanced information networking and applications workshops, international conference on, pp 648–653. doi: 10.1109/WAINA.2009.127
  7. 7.
    Cantador I, Bellogín A, Vallet D (2010) Content-based recommendation in social tagging systems. In: RecSys ’10: proceedings of the fourth ACM conference on recommender systems. ACM, New York, NY, USA, pp 237–240. doi: 10.1145/1864708.1864756 CrossRefGoogle Scholar
  8. 8.
    Carmagnola F, Cena F, Console L, Cortassa O, Ferri M, Gena C, Goy A, Parena M, Torre I, Toso A, Vernero F, Vellar A (2006) icity—an adaptive social mobile guide for cultural events. In: Mobile guide 06Google Scholar
  9. 9.
    Centre for Digital Music—University of London (2007) The event ontology. Available at
  10. 10.
    Cornelis C, Guo X, Lu J, Zhang G (2005) A fuzzy relational approach to event recommendation. In: Proceedings of the 1st Indian international conference on artificial intelligence. Pune, India, pp 2231–2242Google Scholar
  11. 11.
    Cornelis C, Lu J, Guo X, Zhang G (2007) One-and-only item recommendation with fuzzy logic techniques. Inf Sci 177(22):4906–4921. doi: 10.1016/j.ins.2007.07.001, zbMATHCrossRefGoogle Scholar
  12. 12.
    Davidson J, Liebald B, Liu J, Nandy P, Van Vleet T, Gargi U, Gupta S, He Y, Lambert M, Livingston B, Sampath D (2010) The youtube video recommendation system. In: RecSys ’10: proceedings of the fourth ACM conference on recommender systems. ACM, New York, NY, USA, pp 293–296. doi: 10.1145/1864708.1864770 CrossRefGoogle Scholar
  13. 13.
    Hayes C, Massa P, Avesani P, Cunningham P (2002) An on-line evaluation framework for recommender systems. In: In workshop on personalization and recommendation in E-commerce. Malaga, Springer VerlagGoogle Scholar
  14. 14.
    Herlocker J, Konstan J, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd international ACM SIGIR conference on research and development in information retrieval. Berkeley, USA, pp 230–237Google Scholar
  15. 15.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53. doi: 10.1145/963770.963772 CrossRefGoogle Scholar
  16. 16.
    Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans Inf Syst 22(1):116–142. doi: 10.1145/963770.963775 CrossRefGoogle Scholar
  17. 17.
    Huang Z, Zeng D, Chen H (2004) A link analysis approach to recommendation with sparse data. In: AMCIS 2004: Americas conference on information systems. New York, NY, USAGoogle Scholar
  18. 18.
    Huang Z, Zeng D, Chen H (2007) A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intell Syst 22(5):68–78. doi: 10.1109/MIS.2007.80 CrossRefGoogle Scholar
  19. 19.
    International Council of Museums / ICOMs International Committee for Documentation (2009) Definition of the CIDOC conceptual reference model. Available at
  20. 20.
    International Press Telecommunications Council (2009) EventsML-G2 specification—version 1.1. Available at
  21. 21.
    Internet Engineering Task Force (2009) Internet calendaring and scheduling core object specification—iCalendar. Available at
  22. 22.
    Karypis G (2001) Evaluation of item-based top-N recommendation algorithms. In: Proceedings of the 10th international conference on information and knowledge management. Atlanta, USA, pp 247–254Google Scholar
  23. 23.
    Kayaalp M, Özyer T, Özyer ST (2009) A collaborative and content based event recommendation system integrated with data collection scrapers and services at a social networking site. In: ASONAM ’09: proceedings of the 2009 international conference on advances in social network analysis and mining. IEEE Computer Society, Washington, DC, USA, pp 113–118. doi: 10.1109/ASONAM.2009.41 CrossRefGoogle Scholar
  24. 24.
    Klamma R, Cuong PM, Cao Y (2009) You never walk alone: Recommending academic events based on social network analysis. In: Akan O, Bellavista P, Cao J, Dressler F, Ferrari D, Gerla M, Kobayashi H, Palazzo S, Sahni S, Shen XS, Stan M, Xiaohua J, Zomaya A, Coulson G, Zhou J (eds) Complex sciences. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol 4. Springer, Berlin Heidelberg, pp 657–670CrossRefGoogle Scholar
  25. 25.
    Kurapati K, Gutta S, Schaffer D, Martino J, Zimmerman J (2001) A multi-agent TV recommender. In: Proceedings of the 5th international conference on user modeling—workshop personalization in future TV. Sonthofen, Germany, pp 1–8Google Scholar
  26. 26.
    Lee DH (2008) Pittcult: trust-based cultural event recommender. In: RecSys ’08: proceedings of the 2008 ACM conference on recommender systems. ACM, New York, NY, USA, pp 311–314. doi: 10.1145/1454008.1454060 CrossRefGoogle Scholar
  27. 27.
    Linden G, Smith B, York J (2003) recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1):76–80CrossRefGoogle Scholar
  28. 28.
    LinkingOpenData (W3C SWEO Community Project)—Centre for Digital Music (2007) Audioscrobbler RDF Service. Available at
  29. 29.
    Mannens E, Coppens S, De Pessemier T, Geebelen K, Dacquin H, Van de Walle R (2009) Unifying and targeting cultural activities via events modelling and profiling. In: EiMM ’09: proceedings of the 1st ACM international workshop on events in multimedia. ACM, New York, NY, USA, pp 33–40. doi: 10.1145/1631024.1631033 CrossRefGoogle Scholar
  30. 30.
    Marshall C, Rossman G (1999) Designing qualitative research. Sage Publications, London, UKGoogle Scholar
  31. 31.
    McGuinness D, van Harmelen F (eds) (2004) OWL web ontology language: overview. W3C recommendation. World Wide Web Consortium. Available at
  32. 32.
    McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI ’06: CHI ’06 extended abstracts on human factors in computing systems. ACM, New York, NY, USA, pp 1097–1101. doi: 10.1145/1125451.1125659 CrossRefGoogle Scholar
  33. 33.
    Morgan D (1988) Qualitative research methods series, vol 16. Focus groups as qualitative research. Sage Publications, Newbury Park, USAGoogle Scholar
  34. 34.
    Pemberton S (ed) (2002) XHTML 1.0 the extensible hypertext markup language, 2nd edn. W3C recommendation. World Wide Web Consortium. Available at
  35. 35.
    Prud’hommeaux E, Seaborne A (eds) (2007) SPARQL query language for RDF. W3C recommendation. World Wide Web Consortium. Available at
  36. 36.
    Segaran T (2007) Programming collective intelligence, 1st edn. O’Reilly.
  37. 37.
    Shani G (2010) Tutorial on evaluating recommender systems. In: RecSys ’10: proceedings of the fourth ACM conference on recommender systems. ACM, New York, NY, USA, pp 1–1. doi: 10.1145/1864708.1864710 CrossRefGoogle Scholar
  38. 38.
    Shaw R, Troncy R, Hardman L (2009) LODE: linking open descriptions of events. In: Proceedings of the 4th international asian semantic web conference. Shanghai, ChinaGoogle Scholar
  39. 39.
    Wang J, de Vries AP, Reinders MJT (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: SIGIR ’06: proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, NY, USA, pp 501–508. doi: 10.1145/1148170.1148257 CrossRefGoogle Scholar
  40. 40.
    Weng J, Miao C, Goh A, Shen Z, Gay R (2006) Trust-based agent community for collaborative recommendation. In: Proceedings of the 5th international joint conference on autonomous agents and multiagent systems. Hakodate, Japan, pp 1260–1262Google Scholar
  41. 41.
    Yildirim H, Krishnamoorthy MS (2008) A random walk method for alleviating the sparsity problem in collaborative filtering. In: RecSys ’08: proceedings of the 2008 ACM conference on recommender systems. ACM, New York, NY, USA, pp 131–138. doi: 10.1145/1454008.1454031 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Toon De Pessemier
    • 1
    Email author
  • Sam Coppens
    • 2
  • Kristof Geebelen
    • 3
  • Chris Vleugels
    • 4
  • Stijn Bannier
    • 4
  • Erik Mannens
    • 2
  • Kris Vanhecke
    • 1
  • Luc Martens
    • 1
  1. 1.INTEC – WiCaGhent University – IBBTGhentBelgium
  2. 2.ELIS – Multimedia LabGhent University – IBBTGhentBelgium
  3. 3.DistrinetK.U. Leuven – IBBTLeuvenBelgium
  4. 4.SMITVUB – IBBTBrusselsBelgium

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