NDLI: An efficient collaborative filtering algorithm using SVD-free latent Semantic indexing and particle swarm optimization

Content Provider	IEEE Xplore Digital Library
Author	Abdelwahab, A. Sekiya, H. Matsuba, I. Horiuchi, Y. Kuroiwa, S. Nishida, M.
Copyright Year	2009
Description	Author affiliation: Graduate School of Advanced Integration Science, Chiba University, Chiba, Japan (Abdelwahab, A.; Sekiya, H.; Matsuba, I.; Horiuchi, Y.; Kuroiwa, S.) \|\| Department of Information Systems Design, Doshisha University, Kyoto, Japan (Nishida, M.)
Abstract	The amount of accessible information in the Internet increases every day and it becomes greatly difficult to deal with such a huge source of information. Consequently, Recommender Systems (RS) which are considered as powerful tools for Information Retrieval (IR), can access these available information efficiently. Unfortunately, the recommendations accuracy is seriously affected by the problems of data sparsity and scalability. Additionally, the time of recommendations is very essential in the Recommender Systems. Therefore, we propose a proficient dimensionality reduction-based Collaborative Filtering (CF) Recommender System. In this technique, the Singular Value Decomposition-free (SVD-free) Latent Semantic Indexing (LSI) is utilized to obtain a reduced data representation solving the sparsity and scalability limitations. Also, the SVD-free extremely reduce the time and memory usage required for dimensionality reduction employing the partial symmetric Eigenproblem. Moreover, to estimate the optimal number of reduced dimensions which greatly influences the system accuracy, the Particle Swarm Optimization (PSO) algorithm is utilized to automatically obtain it. As a result, the proposed technique enormously increases the recommendations prediction quality and speed. In additions, it decreases the memory requirements. To show the efficiency of the proposed technique, we employed it to the MovieLens dataset and the results was very promising.
Starting Page	1
Ending Page	4
File Size	234678
Page Count	4
File Format	PDF
ISBN	9781424445387
DOI	10.1109/NLPKE.2009.5313754
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	collaborative filtering Information resources Scalability latent semantic indexing Information retrieval Large scale integration particle swarm optimization Particle swarm optimization dimensionality reduction recommender system Collaboration Filtering algorithms Internet Recommender systems singular value decomposition Indexing
Content Type	Text
Resource Type	Article

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