CROSSING FRAMEWORK - A Dynamic Infrastructure to Develop Knowledge-based Recommenders in Cross Domains

Mustafa Azak, Aysenur Birturk

2010

Abstract

We propose a dynamic framework that differs from the previous works as it focuses on the easy development of knowledge-based recommenders and it proposes an intensive cross domain capability with the help of domain knowledge. The framework has a generic and flexible structure that data models and user interfaces are generated based on ontologies. New recommendation domains can be integrated to the framework easily in order to improve recommendation diversity. We accomplish the cross-domain recommendation via an abstraction in domain features if the direct matching of the domain features is not possible when the domains are not very close to each other.

References

  1. Adomavicius, G. and Tuzhilin A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-art and Possible Extension. IEEE Trans. Know. And Data Eng. 17, 6: 734-749.
  2. Anand, S. and Mobasher, B. 2003 Intelligent Techniques for Web Personalization. IJCAI 2003 Workshop.
  3. Berkovsky, S., Kuflik, T. and Ricci, F. 2005. Entertainment Personalization Mechanism through Cross-domain User Modeling. 1st Int'l Conf. Intelligent Tech. for Interactive Ent., 215-219.
  4. Chung, R., Sundaram, D. and Srinivasan, A. 2007. Integrated Personal Recommender Systems. ICEC'07. Minneapolis, Minnesota, USA.
  5. Gonzalez, G., Lluis de la Rosa. J, Dugdale, J., Pavard, B., El Jed, M., Angulo, C. and Klann, M. 2006 Towards Ambient Recommender Systems: Results of New Cross-disciplinary Trends. In Proc. of the EU Conf. on AI, WS on Recommender Systems.
  6. Loizou, A. 2007. Unlocking the Potential of Recommender Systems: A Framework to Achieve Multiple Domain Recommendations. Sch. of Electronics and CS, University of Southampton.
  7. Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. 2001.
  8. Item-based collaborative filtering recommendation algorithms. In Proc. of the 10th Int'l conf. on WWW.
  9. Sebastiani, F. 2002. ML in Aut. TC. ACM Comp. Surv.
  10. Szomszor, M., Cantador, I. and Alani, H. 2008. Correlating User Profiles from Multiple Folksonomies. ACM Conf. on Hypertext and Hypermedia.
Download


Paper Citation


in Harvard Style

Azak M. and Birturk A. (2010). CROSSING FRAMEWORK - A Dynamic Infrastructure to Develop Knowledge-based Recommenders in Cross Domains . In Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 2: WEBIST, ISBN 978-989-674-025-2, pages 125-130. DOI: 10.5220/0002807901250130


in Bibtex Style

@conference{webist10,
author={Mustafa Azak and Aysenur Birturk},
title={CROSSING FRAMEWORK - A Dynamic Infrastructure to Develop Knowledge-based Recommenders in Cross Domains},
booktitle={Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 2: WEBIST,},
year={2010},
pages={125-130},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002807901250130},
isbn={978-989-674-025-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 2: WEBIST,
TI - CROSSING FRAMEWORK - A Dynamic Infrastructure to Develop Knowledge-based Recommenders in Cross Domains
SN - 978-989-674-025-2
AU - Azak M.
AU - Birturk A.
PY - 2010
SP - 125
EP - 130
DO - 10.5220/0002807901250130