Abstract
This paper describes the design and implementation of a new dynamic Web Recommender System using Hard and Fuzzy K-modes clustering. The system provides recommendations based on user preferences that change in real time taking also into account previous searching and behavior. The recommendation engine is enhanced by the utilization of static preferences which are declared by the user when registering into the system. The proposed system has been validated on a movie dataset and the results indicate successful performance as the system delivers recommended items that are closely related to user interests and preferences.
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de Nooij, G.J.: Recommender Systems: An Overview. MSc Thesis, University of Amsterdam (November 2008)
Principia Cybernetica Web, Entropy and Information, http://pespmc1.vub.ac.be/ENTRINFO.html
Stylianou, C., Andreou, A.S.: A Hybrid Software Component Clustering and Retrieval Scheme, Using an Entropy-based Fuzzy k-Modes Algorithm. In: Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, Washinghton DC, USA, vol. 1, pp. 202–209 (2007) ISBN:0-7695-3015-X
Khan, S.S.: Computation of Initial Modes for K-modes Clustering Algorithm. In: Proceedings of the 20th International Joint Conference on Artificial Intelligent, San Francisco, USA, pp. 2784–2789 (2007)
Speigel, S., Kunegis, J., Li, F.: Hydra: A Hybrid Recommender System. In: Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information & Knowledge Management, New York, USD, pp. 75–80 (2009)
Ekstrand, M., Riedl, J.: When recommender fail: predicting recommending failure for algorithm selection and combination. In: Proceedings of the 6th ACM Conference on Recommender Systems, New York, USA, pp. 233–236 (2012)
Jung, J.J., Pham, X.H.: Attribute selection-based recommendation framework for short-head user group: An empirical study by MovieLens and IMDB. In: Proceedings of the Third International Conference on Computational Collective Intelligence: Technologies and Applications, Berlin, Germany, pp. 592–601 (2011)
McNee, S.M., Riedl, J., Konstan, J.A.: Making Recommendations Better: An Analytic Model for Human- Recommender Interaction. In: Proceedings of the CHI 2006 Extended Abstracts on Human Factors in Computing Systems (2006)
Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. In: Proceedings of the 10th International Conference on Information and Knowledge Management, New York, USA, pp. 247–254 (2001)
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011) ISBN-13: 978-0387858197
Van Meteren, R., van Someren, M.: Using Content-Based Filtering for Recommendation. In: Proceedings of the ECML 2000 Workshop: Machine Learning in New Information Age, Barcelona, Spain, pp. 47–57 (2000)
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Christodoulou, P., Lestas, M., Andreou, A.S. (2013). A Dynamic Web Recommender System Using Hard and Fuzzy K-Modes Clustering. In: Papadopoulos, H., Andreou, A.S., Iliadis, L., Maglogiannis, I. (eds) Artificial Intelligence Applications and Innovations. AIAI 2013. IFIP Advances in Information and Communication Technology, vol 412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41142-7_5
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DOI: https://doi.org/10.1007/978-3-642-41142-7_5
Publisher Name: Springer, Berlin, Heidelberg
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