Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

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

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

Abstract

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.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. https://projects.ibbt.be/cupid

  2. http://www.cultuurnet.be

  3. http://www.cultuurdatabank.com/XMLSchema/CdbXSD/3.0/FINAL/CdbXSD.xsd

  4. http://www.last.fm

  5. http://xmlns.com/foaf/spec/

  6. http://www.iptc.org

  7. http://www.iptc.org/cms/site/index.html?channel=CH0088

  8. http://www.w3.org/2004/02/skos/core#

  9. http://www.w3.org/2003/01/geo/wgs84_pos#

  10. http://multimedialab.elis.ugent.be/ontologies/EventsML-G2/v1.0/EventsML.owl

  11. http://www.ietf.org/rfc/rfc2616.txt

  12. http://tools.ietf.org/html/rfc3986

  13. http://virtuoso.openlinksw.com/wiki/main/Main

  14. http://www4.wiwiss.fu-berlin.de/pubby/

  15. http://ontologydesignpatterns.org/wiki/Ontology:DOLCE+DnS_Ultralite

  16. http://www.loa-cnr.it/ontologies/DLP_397.owl

  17. http://www.ontologydesignpatterns.org/ont/cdns/cDnS.owl

  18. http://www.cyc.com/opencyc/overview

  19. http://www.cyc.com/cyc/technology/whatiscyc

  20. http://wordnet.princeton.edu

  21. http://xmlns.com/foaf/spec

  22. http://www.wikipedia.org

  23. http://sw.opencyc.org/downloads/opencyc_owl_downloads_v3/opencyc-latest.owl.gz

  24. http://metadata.net/harmony/ABC/ABC.owl

  25. For more details on this linking, see the eventsML ontology at http://multimedialab.elis.ugent.be/ontologies/EventsML-G2/v1.0/EventsML.owl

  26. http://www.rss-specifications.com/

  27. http://www.iptc.org/cms/site/index.html?channel=CH0103

  28. http://dbpedia.org/

  29. http://opengroup.org/projects/soa/

  30. http://docs.oasis-open.org/wsbpel/

  31. http://www.w3.org/TR/2007/REC-wsdl20-20070626/

  32. http://www.w3.org/TR/rdf-syntax-grammar/

  33. http://netbeans.org/

  34. https://open-esb.dev.java.net/

  35. http://jcp.org/aboutJava/communityprocess/final/jsr208/index.html

  36. http://java.sun.com/javaee/

  37. http://tools.ietf.org/html/rfc2854

  38. http://www.w3.org/TR/xslt

  39. http://www.cultuurdatabank.com/XMLSchema/CdbXSD/3.0/FINAL/CdbXSD.xsd

  40. http://linkeddata.org

  41. http://www.iknow.be

  42. http://www.opencalais.com/

  43. http://www.toerismevlaanderen.be and http://www.visitflanders.co.uk/

  44. http://www.geonames.org

  45. http://www.bibnet.be

  46. VRT, is a publicly-funded broadcaster of radio and television in Flanders, http://www.vrt.be

  47. http://www.amazon.com/

  48. http://www.last.fm/

  49. http://www.YouTube.com/

  50. http://www.pianofiles.com/

  51. http://www.netflixprize.com/

  52. http://aws.amazon.com/elasticloadbalancing

  53. http://aws.amazon.com/simpledb

  54. http://aws.amazon.com/s3

  55. http://aws.amazon.com/sqs

  56. http://wiki.developers.facebook.com/index.php/API

  57. Vooruit is an arts centre in Ghent, Belgium (http://vooruit.be/)

  58. Ancienne Belgique is a concert hall in Brussels, Belgium (http://www.abconcerts.be)

  59. UiTinVlaanderen is an online leisure agenda for Flanders and Brussels, founded by the Flemish Ministry of Culture. (http://www.uitinvlaanderen.be/).

  60. http://openid.net/

  61. http://oauth.net/

References

  1. Beckett D (ed) (2004) RDF/XML syntax specification (revised). W3C recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/rdf-syntax-grammar/

  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 1265–1266

  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 http://www.w3.org/TR/2006/REC-xml-20060816/

  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 43–52

  5. Brickley D (ed) (2004) RDF vocabulary description language 1.0: RDF schema. W3C recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/rdf-schema/

  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. 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

    Chapter  Google Scholar 

  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 06

  9. Centre for Digital Music—University of London (2007) The event ontology. Available at http://purl.org/NET/c4dm/event.owl

  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 2231–2242

    Google Scholar 

  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, http://www.sciencedirect.com/science/article/B6V0C-4P5R62N-3/2/cde1e1d5f5a3a2e663a0f8d2b7a152bc

    Article  MATH  Google Scholar 

  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

    Chapter  Google Scholar 

  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 Verlag

    Google Scholar 

  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 230–237

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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, USA

  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

    Article  Google Scholar 

  19. International Council of Museums / ICOMs International Committee for Documentation (2009) Definition of the CIDOC conceptual reference model. Available at http://cidoc.ics.forth.gr/docs/cidoc_crm_version_5.0.1_Mar09.pdf

  20. International Press Telecommunications Council (2009) EventsML-G2 specification—version 1.1. Available at http://www.iptc.org/std/EventsML-G2/EventsML-G2_1.3.zip

  21. Internet Engineering Task Force (2009) Internet calendaring and scheduling core object specification—iCalendar. Available at http://tools.ietf.org/html/rfc5545

  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 247–254

  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

    Chapter  Google Scholar 

  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 657–670

    Chapter  Google Scholar 

  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 1–8

  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

    Chapter  Google Scholar 

  27. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1):76–80

    Article  Google Scholar 

  28. LinkingOpenData (W3C SWEO Community Project)—Centre for Digital Music (2007) Audioscrobbler RDF Service. Available at http://www.audioscrobbler.net/

  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

    Chapter  Google Scholar 

  30. Marshall C, Rossman G (1999) Designing qualitative research. Sage Publications, London, UK

    Google Scholar 

  31. McGuinness D, van Harmelen F (eds) (2004) OWL web ontology language: overview. W3C recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/owl-features/

  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

    Chapter  Google Scholar 

  33. Morgan D (1988) Qualitative research methods series, vol 16. Focus groups as qualitative research. Sage Publications, Newbury Park, USA

    Google Scholar 

  34. Pemberton S (ed) (2002) XHTML 1.0 the extensible hypertext markup language, 2nd edn. W3C recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/xhtml1/

  35. Prud’hommeaux E, Seaborne A (eds) (2007) SPARQL query language for RDF. W3C recommendation. World Wide Web Consortium. Available at http://www.w3.org/TR/rdf-sparql-query/

  36. Segaran T (2007) Programming collective intelligence, 1st edn. O’Reilly. http://proquestcombo.safaribooksonline.com/9780596529321/

  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

    Chapter  Google Scholar 

  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, China

  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

    Chapter  Google Scholar 

  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 1260–1262

  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

    Chapter  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Toon De Pessemier.

Rights and permissions

Reprints and permissions

About this article

Cite this article

De Pessemier, T., Coppens, S., Geebelen, K. et al. Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform. Multimed Tools Appl 58, 167–213 (2012). https://doi.org/10.1007/s11042-010-0715-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0715-8

Keywords