Abstract

Recommendation systems suggest the personalized list of items such as products, people and activities to reduce the search in a large amount of available information, by filtering the relevant information which the user will prefer to explore more. Currently, many web applications include the recommendation systems to enhance the user’s experience and trust and thereby enable the service provider to retain the customers. Context based recommendation systems are aimed at providing relevant recommendations to users using the context as additional information in computation of recommendation. Context is the information concerning the situation of user interaction with the system along with the information of users and items. The acquisition, storage and representation of context are the requirements of the context based recommendation system. The context modeling deals with the representation of context in a form which can be suitable for storage and access to compute the recommendation. A conceptual model of context and recommendation system using the graphical object oriented model is proposed in this paper. It is converted into a relational database model for storage and access. This model can be used to implement the design of a context based recommendation system in many domains of applications.

References

• Ricci, F., Rokach, L. and Shapira, B., 2011. Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). springer US.
• Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A., 2013. Recommender systems survey. Knowledge-based systems, 46, pp.109-132.
• Adomavicius, G. and Tuzhilin, A., 2005. 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), pp.734-749.
• Ramirez-Garcia, X. and Garcia-Valdez, M., 2015. A Pre-filtering Based Context-Aware Recommender System using Fuzzy Rules. In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization (pp.497-505). Springer International Publishing.
• Cremonesi, P., Garza, P., Quintarelli, E. and Turrin, R., 2011, October. Top-n recommendations on unpopular items with contextual knowledge. In 2011 Workshop on Context-aware Recommender Systems. Chicago.
• Pandey, A.K., Kumar, A. and Rajendran, B., 2013. Contextual Model of Recommending Resources on an Academic Networking Portal. In Proceedings of 3rd International Conference on Computer Science & Information Technology, CCSIT (pp. 421-429).
• Adomavicius, G. and Tuzhilin, A., 2015. Context-aware recommender systems. In Recommender systems handbook(pp. 191-226). Springer US.
• Schilit, B., Adams, N. and Want, R., 1994, December. Context-aware computing applications. In Mobile Computing Systems and Applications, WMCSA 1994. First Workshop on (pp. 85-90). IEEE.
• Schilit, B.N. and Theimer, M.M., 1994. Disseminating active map information to mobile hosts. IEEE network, 8(5), pp.22-32.
• Henricksen, K., Indulska, J. and Rakotonirainy, A., 2002. Modeling context information in pervasive computing systems. Pervasive Computing, pp.79-117.
• Zimmer, T., 2004, March. Towards a better understanding of context attributes. In Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second IEEE Annual Conference on (pp. 23-27). IEEE.
• Abowd, G., Dey, A., Brown, P., Davies, N., Smith, M. and Steggles, P., 1999. Towards a better understanding of context and context-awareness. In Handheld and ubiquitous computing (pp.304-307). Springer Berlin /Heidelberg.
• Dourish, P., 2004. What we talk about when we talk about context. Personal and ubiquitous computing, 8(1), pp.19-30.
• Bauer, C. and Dey, A.K., 2016. Considering context in the design of intelligent systems: Current practices and suggestions for improvement. Journal of Systems and Software, 112, pp.26-47.
• Mcheick, H., 2014. Modeling Context Aware Features for Pervasive Computing. Procedia Computer Science, 37, pp.135-142.
• Strang, T. and Linnhoff-Popien, C., 2004, September. A context modeling survey. In Workshop Proceedings.
• Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A. and Riboni, D., 2010. A survey of context modeling and reasoning techniques. Pervasive and Mobile Computing, 6(2), pp.161-180.
• Adomavicius, G., Sankaranarayanan, R., Sen, S. and Tuzhilin, A., 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23(1), pp.103-145.
• Ai, D.X., Zuo, H. and Yang, J., 2013. Ontology-Based Context Modeling for Mobile Catering Recommendation. In Advanced Materials Research (Vol. 662, pp. 953-956). Trans Tech Publications.
• Choi, D., Kim, N. and Hung, D.T., 2012. Conceptual data modeling for realizing context-aware services. Expert Systems with Applications, 39(3), pp.3022-3030.
• Mettouris, C. and Papadopoulos, G.A., 2013. Contextual modeling in context-aware recommender systems: a generic approach. In Web Information Systems Engineering–WISE 2011 and 2012 Workshops (pp. 41-52). Springer, Berlin, Heidelberg.
• Ambler, S.W., 2000. Mapping objects to relational databases: What you need to know and why. Ronin International.
• Lombardi, S., Gorgoglione, M. and Panniello, U., 2013. The effect of context on misclassification costs in e-commerce applications. Expert Systems with Applications, 40(13), pp.5219-5227.
• Stefanidis, K., Pitoura, E. and Vassiliadis, P., 2005. On supporting context-aware preferences in relational database systems. In International Workshop on Managing Context Information in Mobile and Pervasive Environments.