Ontology Matched Cross Domain Personalized Recommendation of Tourist Attractions
- 5 Downloads
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.
KeywordsRecommender system Ontology Transfer learning Cross domain
- 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
- 11.Sabou, M., Arsal, I., & Bra, A. M. P. (2009). TourMISLOD: A tourism linked data set. Semantic Web, 4(3), 1–5.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).Google Scholar
- 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).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).Google Scholar
- 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.
- 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).Google Scholar
- 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).Google Scholar