Abstract
In this paper we address the problem of propagating user interests in ontology-based user models. Our ontology-based user model (OBUM) is devised as an overlay over the domain ontology. Using ontologies as the basis of the user profile allows the initial user behavior to be matched with existing concepts in the domain ontology. Such ontological approach to user profiling has been proven successful in addressing the cold-start problem in recommender systems, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the ontological structure of the domain. The main contribution of the paper is the novel algorithm for propagation of user interests which takes into account i) the ontological structure of the domain and, in particular, the level at which each domain item is found in the ontology; ii) the type of feedback provided by the user, and iii) the amount of past feedback provided for a certain domain object.
This work has been supported by PIEMONTE Project - People Interaction with Enhanced Multimodal Objects for a New Territory Experience.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Bach, T.-L., Dieng-Kuntz, R.: Measuring Similarity of Elements in OWL DL Ontologies. In: Proc. of the AAAI 2005 Workshop on Contexts and Ontologies: Theory, Practice and Applications (2005)
Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Modeling and User Adapted Interaction 6(2-3), 87–129 (1996)
Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., Torre, I., Toso, A., Vernero, F.: Tag-based user modeling for social multi-device adaptive guides. User Modeling and User-Adapted Interaction 18, 497–538 (2008)
Chiabrando, E., Likavec, S., Lombardi, I., Picardi, C., Theseider Dupré, D.: Semantic similarity in heterogeneous ontologies. In: Proc. of the 22nd ACM Conference on Hypertext and Hypermedia, Hypertext 2011, pp. 153–160. ACM, New York (2011)
Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: GUMO - the General User Model Ontology. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 428–432. Springer, Heidelberg (2005)
Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationship. The Knowledge Engineering Review 16(2), 111–155 (2001)
Mehta, B., Niederée, C., Stewart, A., Degemmis, M., Lops, P., Semeraro, G.: Ontologically-Enriched Unified User Modeling for Cross-System Personalization. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 119–123. Springer, Heidelberg (2005)
Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Transactions on Information Systems 22, 54–88 (2004)
PIEMONTE Team. WantEat: interacting with social networks of intelligent things and people in the world of enogastronomy. In: Interacting with Smart Objects Workshop 2011 (2011)
Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Transactions on Systems Management and Cybernetics 19(1), 17–30 (1989)
Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based user modeling for knowledge management systems. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 213–217. Springer, Heidelberg (2003)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proc. of the 14th Int. Joint Conference on Artificial Intelligence, pp. 448–453 (1995)
Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)
Rocha, C., Schwabe, D., Aragao, M.P.: A hybrid approach for searching in the semantic web. In: Proc. of the 13th Int. Conference on World Wide Web, WWW 2004, pp. 374–383. ACM, New York (2004)
Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proc. of the 16th ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 525–534. ACM, New York (2007)
Sieg, A., Mobasher, B., Burke, R.: Improving the effectiveness of collaborative recommendation with ontology-based user profiles. In: Proc. of the 1st Int. Workshop on Information Heterogeneity and Fusion in Recommender Systems, HetRec 2010, pp. 39–46. ACM, New York (2010)
Stevens, S.: On the psychophysical law. Psychological Review 64(3), 153–181 (1957)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cena, F., Likavec, S., Osborne, F. (2011). Propagating User Interests in Ontology-Based User Model. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-23954-0_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23953-3
Online ISBN: 978-3-642-23954-0
eBook Packages: Computer ScienceComputer Science (R0)