Skip to main content
SpringerLink
Log in

Ontology Matched Cross Domain Personalized Recommendation of Tourist Attractions

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In this era of data deluge, recommender system lists the most likely preferred items to the users. With the vast amount of information, personalization of recommendation is a challenge. Domain knowledge plays a vital role in filtering the data for personalized recommendation. Certain domains does not have sufficient history of data to provide effective recommendation to the users. In such cases, knowledge from a relative domain is transferred to make effective recommendations. The proposed cross domain recommender system deduces relatedness between domains for knowledge transfer. Grouping the users into clusters of similar tastes works best in providing recommendation in real time environment. The proposed novel clustering based transfer learning algorithm incorporates content and collaborative properties of items and users for providing cross domain recommendation. The experiments are conducted with real world dataset which show that transfer learning technique improves the efficiency of recommendation in a sparse domain.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Pan, S. J., Tsang, I. W., Kwok, J. T., & Yang, Q. (2011). Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2), 199–210.

    Article  Google Scholar 

  2. Blanco-Fernndez, Y., Lpez-Nores, M., Pazos-Arias, J. J., Gil-Solla, A., & Ramos-Cabrer, M. (2010). Exploiting digital TV users preferences in a tourism recommender system based on semantic reasoning. IEEE Transactions on Consumer Electronics, 56(2), 904–912.

    Article  Google Scholar 

  3. Soares, M., & Viana, P. (2014). TV recommendation and personalization systems: Integrating broadcast and video on-demand services. Advances in Electrical and Computer Engineering, 14(1), 115–120.

    Article  Google Scholar 

  4. Nair, B. B., & Mohandas, V. P. (2015). An intelligent recommender system for stock trading. Intelligent Decision Technologies, 9(3), 243–269.

    Article  Google Scholar 

  5. 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 

  6. Shapira, B., Rokarch, L., & Freilikhman, S. (2013). Facebook single and cross domain data for recommendation systems. Springer Journal for User Model User-Adaptation, 23(2–3), 211–247.

    Article  Google Scholar 

  7. Doan, A., Madhavan, J., Domingos, P., & Halevy, A. (2004). Ontology matching: A machine learning approach. In S. Staab & R. Studer (Eds.), Handbook on ontologies. International handbooks on information systems (pp. 385–403). Berlin: Springer.

    Google Scholar 

  8. Cardoso, J. (2006). Developing an owl ontology for E-tourism. Springer Semantic Web Services, Processes and Applications, 3(247), 282.

    Google Scholar 

  9. Daramola, O., Adigun, M., & Ayo, C. (2009). Building an ontology-based framework for tourism recommendation services. In W. Hpken, U. Gretzel, & R. Law (Eds.), Information and communication technologies in tourism (pp. 135–147). Vienna: Springer.

    Google Scholar 

  10. Ma, J., Wei, X., Sun, Y., Turban, E., Wang, S., & Liu, O. (2012). An ontology-based text-mining method to cluster. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 42(3), 784–790.

    Article  Google Scholar 

  11. Sabou, M., Arsal, I., & Bra, A. M. P. (2009). TourMISLOD: A tourism linked data set. Semantic Web, 4(3), 1–5.

    Google Scholar 

  12. Chantrapornchai, C., & Choksuchat, C. (2016). Ontology construction and application in practice case study of health tourism in Thailand. SpringerPlus. https://doi.org/10.1186/s40064-016-3747-3.

    Article  Google Scholar 

  13. Terziev, Y., Benner-Wickner, M., Brckmann, T., & Gruhn, V. (2015). Ontology-based recommender system for information support in knowledge-intensive processes. In i-KNOW’15 Proceeding of the 15th international conference on knowledge technologies and data-driven business. https://doi.org/10.1145/2809563.28096.

  14. Milo, T., & Zohar, S. (1998). Using schema matching to simplify heterogeneous data translation. In: VLDB ’98 proceeding of the 24th international conference on very large data bases (pp. 122–133).

  15. Madhavan, J., Bernstein, P. A., & Rahm, E. (2001). Generic schema matching with cupid. In Proceeding of VLDB ’01 27th international conference on very large data bases (pp. 49–58).

  16. Kim, H., Kang, S., & Sangyoon, O. (2015). Ontology based quantitative similarity metric for event matching in publish/subscribe system. Neurocomputing, 152, 77–84.

    Article  Google Scholar 

  17. Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.

    Article  Google Scholar 

  18. Dai, W., Xue, G., Yang, Q., & Yu, Y. (2007). Co-clustering based classification for out-of-domain documents. In 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 210–219).

  19. Thendral, S. E., & Valliyammai, C. (2016). Clustering based transfer learning in cross domain recommender system. In 8th international conference on advanced computing (ICoAC). https://doi.org/10.1109/ICoAC.2017.7951744.

  20. Shi, L., Lin, F., Yang, T., Qi, J., Ma, W., & Shoukun, X. (2014). Context-based ontology-driven recommendation strategies for tourism in ubiquitous computing. Wireless Personal Communications, 76, 731–745.

    Article  Google Scholar 

  21. Cheng, S.-T., Chou, C.-L., & Horng, G.-J. (2013). The adaptive ontology-based personalized recommender system. Wireless Personal Communications, 72(4), 18011826.

    Google Scholar 

  22. Cremonesi, P., Koren, Y., & Turrin, R. (2016). Performance of recommender algorithms on Top-n recommendation tasks. In RecSys’10 Proceeding of the 4th ACM conference on recommender systems (pp. 39–46).

  23. Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. T. (1999). An algorithmic framework for performing collaborative filtering. In ACM SIGIR conference on research and development in information retrieval (pp. 230–237).

Download references

Author information

Authors and Affiliations

  1. Department of Computer Technology, Anna University, MIT Campus, Chennai, India

    C. Valliyammai & S. Ephina Thendral

Authors
  1. C. Valliyammai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Ephina Thendral
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Valliyammai.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valliyammai, C., Ephina Thendral, S. Ontology Matched Cross Domain Personalized Recommendation of Tourist Attractions. Wireless Pers Commun 107, 589–602 (2019). https://doi.org/10.1007/s11277-019-06290-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06290-5

Keywords

Search

Navigation