Abstract
E-commerce applications are popular as a requirement of emerging information and are becoming everyone’s choice for seeking information and expressing opinions through reviews. Recommender systems plays a key role in serving the user with the best Web services by suggesting probable liked items or pages that keeps user out of the information overload problem. Past research of the recommenders mostly focused on improving the quality of suggestions by the user’s navigational patterns in history, but not much emphasis has been given on the concept drift of the user in the current session. In this paper, a new recommender model is proposed that not only identifies the access sequence of the user according to the domain knowledge, but also identifies the concept drift of the user and recommends it. The proposed approach is evaluated by comparing with existing algorithms and perhaps does not sacrifice the accuracy of the quality of the recommendations.
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References
G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Trans. Knowledge Data Eng., vol. 17, no. 6, pp. 734–749, June 2005.
M. Pazzani and D. Billsus, “Content-based recommendation systems,” in The Adaptive Web, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin Heidelberg, Germany: Springer-Verlag, 2007, pp. 325–341.
M. Venu Gopalachari, P. Sammulal, “Personalized Web Page Recommender System using integrated Usage and Content Knowledge”, in the proceedings of 2014 IEEE ICACCCT, 2014. pp. 1066–1071.
J. Schafer, D. Frankowski, J. Herlocker, and S. Sen, “Collaborative filtering recommender systems,” in The Adaptive Web, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin Heidelberg, Germany: Springer-Verlag, 2007, pp. 291–324.
E. Amolochitis, I. T. Christou, and Z. H. Tan, “Implementing a commercial-strength parallel hybrid movie recommendation engine,” IEEE Intell. Syst., vol. 29, no. 2, pp. 92–96, Mar. 2014.
M. Venu Gopalachari, P. Sammulal, “Hybrid Recommender System with Conceptualization and Temporal Preferences”, Proceedings of the Second International Conference on Computer and Communication Technologies, AISC 380, pp. 811–819, Springer, 2015.
M. Jahrer, A. Töscher, and R. Legenstein, “Combining predictions for accurate recommender systems,” in Proc. 16th ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, New York, 2010, pp. 693–702.
S. Vargas and P. Castells, “Rank and relevance in novelty and diversity metrics for recommender systems,” in Proc. 5th ACM Conf. Recommender System, New York, 2011, pp. 109–116.
M. Zhang and N. Hurley, “Avoiding monotony: Improving the diversity of recommendation lists,” in Proc. ACM Conf. Recommender Systems, New York, 2008, pp. 123–130.
G. Adomavicius and Y. Kwon, “Improving aggregate recommendation diversity using ranking-based techniques,” IEEE Trans. Knowledge Data Eng., vol. 24, no. 5, pp. 896–911, May 2012.
F. Fouss and M. Saerens, “Evaluating performance of recommender systems: An experimental comparison,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intelligent Agent Technology, Washington, D.C.: IEEE Computer Society, 2008, pp. 735–738.
B. Mobasher, “Data Mining for Web Personalization,” in The Adaptive Web. vol. 4321, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds.: Springer-Verlag Berlin, Heidelberg, 2007, pp. 90–135.
C. I. Ezeife and Y. Lu, “Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree,” Data Mining and Knowledge Discovery, vol. 10, pp. 5–38, 2005.
S. T. T. Nguyen, “Efficient Web Usage Mining Process for Sequential Patterns,” in Proceedings of the 11th International Conference on Information Integration and Web-based Applications and Services, Kuala Lumpur, Malaysia 2009, pp. 465–469.
L. Wei and S. Lei, “Integrated Recommender Systems Based on Ontology and Usage Mining,” in Active Media Technology. vol. 5820, J. Liu, J. Wu, Y. Yao, and T. Nishida, Eds.: Springer-Verlag Berlin Heidelberg, 2009, pp. 114–125.
S. Salin and P. Senkul, “Using Semantic Information for Web Usage Mining based Recommendation,” in 24th International Symposium on Computer and Information Sciences, 2009., 2009, pp. 236–241.
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Sammulal, P., Venu Gopalachari, M. (2017). A Personalized Recommender System Using Conceptual Dynamics. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_21
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DOI: https://doi.org/10.1007/978-981-10-2471-9_21
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