Improved scalable recommender system.

S. Ishtiaq; N. Majeed; M. Maqsood; A. Javed

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Improved scalable recommender system.

Author(s)

S. Ishtiaq Department of Software Engineering, University of Engineering and Technology (UET), Taxila, Pakistan

N. Majeed Department of Software Engineering, University of Engineering and Technology (UET), Taxila, Pakistan

M. Maqsood Department of Software Engineering, University of Engineering and Technology (UET), Taxila, Pakistan

A. Javed Department of Software Engineering, University of Engineering and Technology (UET), Taxila, Pakistan

Abstract

Recommender systems are known for their ability to recommend items which are new to the user by having some synchronization with user`s personal interest. The importance of recommender systems leads to the creation of new approaches that can produce accurate results. As data became large it results in scalability issues. In this work, we have suggested a scalable technique using different methods that work in a sequential manner. A novel centroid selection for clustering based recommender system is proposed. SVD and user representatives are used to handle scalability issues. Experiments on proposed approach with standard datasets showed great improvement in scalability and slight better accuracy.

Publication Details

Page(s) 200-207

DOI DOI not available

Published Journal: Nucleus, Volume: 53, Issue: 3, Year: 2016

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