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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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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