A Personalized Recommender System Using Conceptual Dynamics
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 ser
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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. Keywords Recommender system dynamics
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Ontology
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Usage patterns
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Conceptual
1 Introduction Internet has left a significant mark in all fields, such as e-commerce, science and technology, education and research, and telecommunication. From the past couple of decades, the research and development of the Web services hasbecome exponential and accelerated by many cutting edge technologies such as big data and P. Sammulal (✉) Jawaharlal Nehru Technological University, Hyderabad College of Engineering, Jagtial, Telangana, India e-mail: [email protected] M. Venu Gopalachari Chaitanya Bharathi Institute of Technology, Hyderabad, India e-mail: [email protected] © Springer Science+Business Media Singapore 2017 S.C. Satapathy et al. (eds.), Proceedings of the First International Conference on Computational Intelligence and Informatics, Advances in Intelligent Systems and Computing 507, DOI 10.1007/978-981-10-2471-9_21
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cloud computing. Popular service providers on the Web such as Netflix, Last.fm music and Amazon are trying to promise satisfaction to their customers by predicting their interests toward the domain by means of recommender systems. Recommender systems are the providers of personalized recommendations that exist in various types with respect to the strategy used, out of which the first one is content based (CB), in which recommenders try to analyze the users’ access sequence; the second one is collaborative filtering (CF) that tries to aggregate the interests of the neighbors of a user. Over a period, hybrid recommenders evolved that combined the features of CB and CF to make suggestions better. The recommenders generally focus on the patterns of the navigation sequence of the customer by means of the user’s past history. The log file in the server can be the source of finding the access patterns of a user under various types of criteria. Broadly, there are two issues identified in this scenario where the first one is if these patterns do not consider the true semantics behind the access patterns, then the outcom
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