Skip to main content
SpringerLink
Log in

Crowdsourcing Based Fuzzy Information Enrichment of Tourist Spot Recommender Systems

  • Conference paper
  • First Online:
Computational Science and Its Applications -- ICCSA 2015 (ICCSA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9158))

Included in the following conference series:

Abstract

Tourist Spot Recommender Systems (TSRS) help users to find the interesting locations/spots in vicinity based on their preferences. Enriching the list of recommended spots with contextual information such as right time to visit, weather conditions, traffic condition, right mode of transport, crowdedness, security alerts etc. may further add value to the systems. This paper proposes the concept of information enrichment for a tourist spot recommender system. Proposed system works in collaboration with a Tourist Spot Recommender System, takes the list of spots to be recommended to the current user and collects the current contextual information for those spots. A new score/rank is computed for each spot to be recommender based on the recommender’s rank and current context and sent back to the user. Contextual information may be collected by several techniques such as sensors, collaborative tagging (folksonomy), crowdsourcing etc. This paper proposes an approach for information enrichment using just in time location aware crowdsourcing. Location aware crowdsourcing is used to get current contextual information about a spot from the crowd currently available at that spot. Most of the contextual parameters such as traffic conditions, weather conditions, crowdedness etc. are fuzzy in nature and therefore, fuzzy inference is proposed to compute a new score/rank, with each recommended spot. The proposed system may be used with any spot recommender system, however, in this work a personalized tourist spot recommender system is considered as a case for study and evaluation. A prototype system has been implemented and is evaluated by 104 real users.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Article  Google Scholar 

  2. Brabham, D.C.: Crowdsourcing as a model for problem solving an introduction and cases. Convergence: The International Journal of Research into New Media Technologies 14(1), 75–90 (2008)

    Google Scholar 

  3. Cao, L., Luo, J., Gallagher, A., Jin, X., Han, J., Huang, T.S.: Aworldwide tourism recommendation system based on geotaggedweb photos. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2274–2277. IEEE (2010)

    Google Scholar 

  4. Casey, S., Kirman, B., Rowland, D.: The gopher game: a social, mobile, locative game with user generated content and peer review. In: Proceedings of the International Conference on Advances in Computer Entertainment Technology, pp. 9–16. ACM (2007)

    Google Scholar 

  5. De Meo, P., Quattrone, G., Ursino, D.: A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy. User Modeling and User-Adapted Interaction 20(1), 41–86 (2010)

    Article  Google Scholar 

  6. Eagle, N.: txteagle: mobile crowdsourcing. In: Aykin, N. (ed.) IDGD 2009. LNCS, vol. 5623, pp. 447–456. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  7. Gavalas, D., Kenteris, M.: A web-based pervasive recommendation system for mobile tourist guides. Personal and Ubiquitous Computing 15(7), 759–770 (2011)

    Article  Google Scholar 

  8. Grant, L., Daanen, H., Benford, S., Hampshire, A., Drozd, A., Greenhalgh, C.: MobiMissions: the game of missions for mobile phones, p. 12. ACM (2007)

    Google Scholar 

  9. Horozov, T., Narasimhan, N., Vasudevan, V.: Using location for personalized POI recommendations in mobile environments. In: International Symposium on Applications and the Internet, SAINT 2006, pp. 124–129. IEEE (2006)

    Google Scholar 

  10. Howe, J.: Crowdsourcing: A definition, crowdsourcing: tracking the rise of the amateur (2006)

    Google Scholar 

  11. Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  Google Scholar 

  12. Konomi, S., Thepvilojanapong, N., Suzuki, R., Pirttikangas, S., Sezaki, K., Tobe, Y.: Askus: amplifying mobile actions. In: Tokuda, H., Beigl, M., Friday, A., Brush, A., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 202–219. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  13. Lu, X., Wang, C., Yang, J.M., Pang, Y., Zhang, L.: Photo2trip: generating travel routes from geo-tagged photos for trip planning. In: Proceedings of the International Conference on Multimedia, pp. 143–152. ACM (2010)

    Google Scholar 

  14. Matyas, S., Matyas, C., Schlieder, C., Kiefer, P., Mitarai, H., Kamata, M.: Designing location-based mobile games with a purpose: collecting geospatial data with CityExplorer. In: Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology, pp. 244–247. ACM (2008)

    Google Scholar 

  15. Takeuchi, Y., Sugimoto, M.: An outdoor recommendation system based on user location history. In: Proceedings of the 1st International Workshop on Personalized Context Modeling and Management for UbiComp Applications, pp. 91–100 (2005)

    Google Scholar 

  16. Tiwari, S., Kaushik, S.: A non functional properties based web service recommender system. In: 2010 International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1–4. IEEE (2010)

    Google Scholar 

  17. Tiwari, S., Kaushik, S.: Information enrichment for tourist spot recommender system using location aware crowdsourcing. In: 2014 IEEE 15th International Conference on Mobile Data Management (MDM), vol. 2, pp. 11–14. IEEE (2014)

    Google Scholar 

  18. van Setten, M., Pokraev, S., Koolwaaij, J.: Context-aware recommendations in the mobile tourist application COMPASS. In: De Bra, P.M., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 235–244. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. www.wikipedia.org/

  20. www.suggestions.yahoo.com/

  21. www.threadless.com/

  22. www.istockphoto.com/

  23. www.innocentive.com/

  24. www.thesheepmarket.com/

  25. www.flickr.com/

  26. www.mturk.com/

  27. www.maps.google.com/

  28. www.fashism.com/

Download references

Author information

Authors and Affiliations

  1. School of IT, IIT Delhi, New Delhi, 110016, India

    Sunita Tiwari

  2. Department of Computer Science and Engineering, IIT Delhi, New Delhi, 110016, India

    Saroj Kaushik

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India

    Sunita Tiwari

Authors
  1. Sunita Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saroj Kaushik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sunita Tiwari .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Canaanland, Nigeria

    Sanjay Misra

  4. University of Calgary, Calgary, Alberta, Canada

    Marina L. Gavrilova

  5. University of Minho, Braga, Portugal

    Ana Maria Alves Coutinho Rocha

  6. Polytechnic University, Bari, Italy

    Carmelo Torre

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Tiwari, S., Kaushik, S. (2015). Crowdsourcing Based Fuzzy Information Enrichment of Tourist Spot Recommender Systems. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9158. Springer, Cham. https://doi.org/10.1007/978-3-319-21410-8_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21410-8_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21409-2

  • Online ISBN: 978-3-319-21410-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation