Abstract
Nowadays, there is an increasing development of intelligent systems like online social networks, personalized recommendation systems and knowledge-based systems which are especially based on ontologies. Personalized recommendation systems applied with online social networking assist delivering a personalized content to Web-based application users. Indeed, these systems offer services that can greatly improve the response to users’ needs in their search for persons or for some products. In order to model these users, semantic web technologies such as ontologies are used to explicit the hidden knowledge through using rules. In this paper, we propose to measure the similarity between the user context and other users’ contexts in our ontology. Then, we integrate this measure in recommendation model to infer recommendation items (raw material, production tool, supplier name, etc.) based on SWRL rules. The experiments and evaluations show the applicability of our approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ameen, A., Khan, K.U.R., Rani, B.P.: SemRPer - a rule based personalization system for semantic web. Int. J. Web Appl. 7(1), 23–38 (2015)
Dey, A.K., Abowd, G.D., Wood, A.: CyberDesk: a framework for providing self-integrating context-aware services. Knowl.-Syst. 11(1), 3–13 (1998)
Hannech, A., Adda, M., Mcheick, H.: Recommendation model based on a contextual similarity measure. In: 15th IEEE International Conference on Machine Learning and Applications, 18–20 December, Anaheim, CA, USA, pp. 394–401 (2016)
Hudli, S., Arvind, H.: Learning in rule-based recommendation systems. In: The 27th Annual IEEE Software Technology Conference Long Beach, California, USA, 12–15 October 2015
Li, M., Chen, X., Li, X., Ma, B., Vitanyi, P.M.B.: The similarity metric. IEEE Trans. Inf. Theory 50(12), 3250–3264 (2004)
Liu, L., Lécué, F., Mehandjiev, N., Xu, L.: Using context similarity for service recommendation. In: The Proceedings of the 4th IEEE International Conference on Semantic Computing, 22–24 September, USA, pp. 277–284 (2010)
Maalej, M., Mtibaa, A., Gargouri, F.: Ontology-based user modeling for handicraft woman recommendation. In: Ait Ameur, Y., Bellatreche, L., Papadopoulos, G.A. (eds.) MEDI 2014. LNCS, vol. 8748, pp. 138–145. Springer, Cham (2014). doi:10.1007/978-3-319-11587-0_14
Nielsen, J.: Why you only need to test with 5 users, 19 March 2000. http://www.useit.com/alertbox
Vanderdonckt, J., Grolaux, D., Van Roy, P., Limbourg, Q., Macq, B., Michel, B.: A design space for context-sensitive user interfaces. In: Proceedings of IASSE (2005)
Acknowledgements
We are very thankful to the Algerian Tunisian Project dealing with the improvement of handicraft women business in emerging countries through affordable technologies and social networks.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Maalej, M., Mtibaa, A., Gargouri, F. (2017). Context Similarity Measure for Knowledge-Based Recommendation System. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2017. Lecture Notes in Computer Science(), vol 10451. Springer, Cham. https://doi.org/10.1007/978-3-319-66805-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-66805-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66804-8
Online ISBN: 978-3-319-66805-5
eBook Packages: Computer ScienceComputer Science (R0)