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Improving recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

Computer Systems Science & Engineering 2023年第8期46卷 1775-1787页

作者： R.Sujatha T.Abirami Department of Information Technology M.Kumarasamy College of EngineeringKarurTamilnaduIndia Department of Information Technology Kongu Engineering CollegePerunduraiTamilnaduIndia

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or *** recommendation agents play a crucial role in personalizing the needs of individual ***,it is essential to improve the user *** recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media *** Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative *** CARS will generate more recommendations utilizing adapting them to a certain contextual situation of ***,the key issue is how contextual information is used to create good and intelligent recommender *** paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated *** ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in ***,the most appropriate contexts in which a user should choose an item or service are *** work converts every label set into a Multi-Label Classification(MLC)problem to enhance *** results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa ***,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

关键词： recommendation agents context-aware recommender systems collaborative recommendation personalization systems optimized neural network-based contextual recommendation algorithm

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