![](bg7.png)
to make more tests and benchmarks to quantify more
precisely the relevance of our system. Moreover, we
could improve the quality of the propositions by
taking into account some group of users as it is done
in collaborative filtering recommender systems. It is
possible by adding a group model into the
architecture. Thus, the recommender system would
become a hybrid recommender system.
ACKNOWLEDGEMENTS
This research is done in collaboration with Côte-
d’Or Tourisme, a company which aims to promote
tourism in its region.
REFERENCES
Brusilovsky, P. 2001. Adaptive hypermedia, User
modeling and user-adapted interaction, 2001 –
Springer.
Cantador, I. and Castells, P. 2006. A multilayered
ontology-based user profiles and semantic social
networks for recommender systems. In 2
nd
International Workshop on Web Personalisation
Recommender Systems and Intelligent User Interfaces
in Conjunction with 7th International Conference in
Adaptive Hypermedia.
Sieg, A., Mobasher, B. and Burke, R. 2007. Leaning
Ontology-Based User Profiles: A Semantic Approach
to Personalized Web Search. In IEEE Intelligent
Informatics Bulletin, vol.8, no. 1.
Cristea, A. and De Mooij, A. 2003. LAOS: Layered
WWW AHS authoring model and their corresponding
algebraic operators, Proceedings of World Wide Web
International Conference, New York, NY: ACM.
De Bra, P., Houben, G-J. and Wu, H. 1999. AHAM: A
dexter-based reference model for adaptive
hypermedia, in Hypertext’99: Proceedings of the 10th
ACMConference on Hypertext and Hypermedia:
Returning to our Diverse Roots, New York, pp.147-
156.
Goldberg, D., Nichols, D., Oki, B. M. and Terry, D. 1992.
Using collaborative filtering to weave an information
tapestry. Communications of the Association for
Computing Machinery, 35(12):61–70.
Hendrix, M. and Cristea, 2008. A. Meta-levels of
adaptation in education, Proceedings of 11th IASTED
International Conference on Computers and Advanced
Technology in Education, V. Uskov (Ed.), IASTED.
Henze, N. 2000. Adaptive hyperbooks: Adaptation for
project-based learning resources. PhD Dissertation,
University of Hannover.
Jamali, M. and Ester, M. 2009. TrustWalker : a Random
Walk Model for Combining Trust-Based and Item-
Based Recommendation. In KDD ’09 : Proceedings of
the 15th ACM SIGKDD international conference on
Knowledge discovery and data mining, New York,
NY, USA, pp. 397–406. ACM.
Kamba, T., Sakagami, H. and Koseki, Y. 2007.
Antagonomy: A Personalized Newspaper on the
World Wide Web, Int’l J. Human-Computer Studies.
Kirkpatrick, S., Gelatt C. D. and Vecchi, M. P. 1983
Optimization by simulated annealing, Science 220
(4598), 671-680.
Liberman, H. 1995. Letizia: An Agent that Assists Web
Browsing. In Proceedings of the Fourteenth
International Joint Conference on Artificial
Intelligence (IJCAI-95), pp 924--929, Morgan
Kaufmann publishers Inc, Montreal, Canada.
Middleton, S., Alani, H., Shadbolt, N. and De Roure, D.
2002. Exploiting synergy between ontologies and
recommender systems. In Proceedings of the WWW
international workshop on the semantic web, v. 55 of
CEUR workshop proceedings, Maui, HW, USA.
Minio, M. and Tasso, C. 1996. User Modeling for
Information Filtering on INTERNET Services:
Exploiting an Extended Version of the UMT Shell. In
UM96 Workshop on User Modeling for Information
Filtering on the WWW; Kailua-Kona, Hawaii, January
2-5.
Mcsherry, D. 2005. Explanation in recommender systems.
Artificial Intelligence Review, 24(2):179 – 197, 2005.
Oard, D. 1997. The State of the Art in Text Filtering User
Modeling and User Adapted Interaction, 7(3)141-178.
Tintarev, N. and Masthoff, J. 2007. A survey of
explanations in recommender systems,
ICDE'07
Workshop on Recommender System.
Zhang, Y. Y. and Jiao, J. X. 2007. An associative
classification-based recommendation system for
personalization in B2C e-commerce application,
Expert Systems with Applications 33 (1) 357–367.
AN ONTOLOGY-BASED APPROACH TO PROVIDE PERSONALIZED RECOMMENDATIONS USING A
STOCHASTIC ALGORITHM
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