NDLI: An Improved Collaborative Recommender System

Content Provider	IEEE Xplore Digital Library
Author	Devi, M.K.K. Venkatesh, P.
Copyright Year	2009
Abstract	Collaborative Filtering is widely used in the recommendation system which gives recommendation based on the similar liking users. The Collaborative Recommender System (CR) suffers from sparsity and cold-start problems. We design and implementation an intelligent kernel based Fuzzy Collaborative Recommender System for map user needs to products that can satisfy them based on ratings. In this paper we present an intelligent, smoothened, fuzzy kernel based approach to solving this problem. The system is intelligent, so the user gets recommendation based on fuzzy similarity with the neighbours. The approach is smoothened, so it uses radial basis function network to smooth the sparse rating matrix. In this manner, it allows the efficient determination of products that meet the user’s requirements. Experimental results based on the Movie lens dataset show that the proposed system only by smoothening provides more than 95% accuracy for existing users in the highly sparse data. The results proved that the proposed system outperforms existing systems such as Support Vector Machine, Multi Layer Perceptron, Bayesian, and Singular Value Decomposition, in terms of accuracy and relevance of recommendations. Finally the system is developed for our college library book application and it is tested by our college students.
Starting Page	386
Ending Page	391
File Size	396401
Page Count	6
File Format	PDF
ISBN	9781424453641
DOI	10.1109/NetCoM.2009.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Educational institutions Sparse matrices Fuzzy radial basis function network Collaborative filtering Sparsity Collaboration Radial basis function networks Chromium Cold start problem Kernel Intelligent systems Recommender systems Fuzzy systems
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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