Abstract
Mobile advertisement causes an information overload problem that is addressed by information filtering systems. Semantical filtering systems stand out in comparison to traditional approaches thanks to their use of ontologies as knowledge model improving automatic user profiling and content matching processes in filtering. This position paper identifies some enhancement opportunities related to these two processes, manifold: The formulation of a semantic similarity metric that points out the importance of the relations and properties present in the knowledge domain and a extension in the contextual information included so far in filtering systems. The expected result of the work is to improve the overall effectiveness of semantic information filtering systems, tested in the mobile advertisement scenario.
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
Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5, 4–7 (2001)
Hanani, U., Shapira, B., Shoval, P.: Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction 11, 203–259 (2001)
Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5(2), 199–220 (1993)
Huang, Y., Garcia-Molina, H.: Publish/subscribe in a mobile environment. Wireless Networks 10(6), 643–652 (2004)
Crestani, F.: Application of spreading activation techniques in information retrieval. Artif. Intell. Rev. 11(6), 453–482 (1997)
Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)
Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: Proceedings of the sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM 2007, pp. 525–534. ACM Press, New York (2007)
Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., López-Nores, M., García-Duque, J., Fernández-Vilas, A., Díaz-Redondo, R.P.: Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study. J. Syst. Softw. 81(12), 2371–2385 (2008)
Vallet, D., Cantador, I., Fernández, M., Castells, P.: A multi-purpose ontology-based approach for personalized content filtering and retrieval. In: Semantic Media Adaptation and Personalization, International Workshop on, vol. 0, pp. 19–24 (2006)
Jiang, X., Tan, A.H.: Learning and inferencing in user ontology for personalized semantic web search. Information Sciences 179(16), 2794–2808 (2009)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng. 17(6), 734–749 (2005)
Cantador, I., Bellogín, A., Castells, P.: Ontology-based personalised and context-aware recommendations of news items. In: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2008, pp. 562–565. IEEE Computer Society Press, Los Alamitos (2008)
Shoval, P., Maidel, V., Shapira, B.: An ontology- content-based filtering method. International Journal on Information Theories and Applications 15, 303–318 (2008)
Albertoni, R., De Martino, M.: Asymmetric and context-dependent semantic similarity among ontology instances. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 1–30. Springer, Heidelberg (2008)
Strang, T., Linnhoff-Popien, C.: A context modeling survey. In: Workshop on Advanced Context Modelling, Reasoning and Management. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, Springer, Heidelberg (2004)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moreno, A., Castro, H. (2010). Context Semantic Filtering for Mobile Advertisement. In: Meersman, R., Dillon, T., Herrero, P. (eds) On the Move to Meaningful Internet Systems: OTM 2010 Workshops. OTM 2010. Lecture Notes in Computer Science, vol 6428. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16961-8_93
Download citation
DOI: https://doi.org/10.1007/978-3-642-16961-8_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16960-1
Online ISBN: 978-3-642-16961-8
eBook Packages: Computer ScienceComputer Science (R0)