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Enhanced SVD for Collaborative Filtering

Lookup NU author(s): Dr Yu GuanORCiD


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Matrix factorization is one of the most popular techniques for prediction problems in the fields of intelligent systems and data mining. It has shown its effectiveness in many real-world applications such as recommender systems. As a collaborative filtering method, it gives users recommendations based on their previous preferences (or ratings). Due to the extreme sparseness of the ratings matrix, active learning is used for eliciting ratings for a user to get better recommendations. In this paper, we propose a new matrix factorization model called Enhanced SVD (ESVD) which combines the classic matrix factorization method with a specific rating elicitation strategy. We evaluate the proposed ESVD method on the Movielens data set, and the experimental results suggest its effectiveness in terms of both accuracy and efficiency, when compared with traditional matrix factorization methods and active learning methods.

Publication metadata

Author(s): Guan X, Li CT, Guan Y

Editor(s): Bailey J., Khan L., Washio T., Dobbie G., Huang J., Wang R.

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Knowledge Discovery and Data Mining (PAKDD 2016)

Year of Conference: 2016

Pages: 503-514

Online publication date: 12/04/2016

Acceptance date: 02/04/2016

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-319-31750-2_40

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783319317496