A multidimensional collaborative filtering fusion approach with dimensionality reduction

Tang, Xiaoyu, Xu, Yue, Abdel-Hafez, Ahmad, & Shlomo, Geva (2014) A multidimensional collaborative filtering fusion approach with dimensionality reduction. In AusDM 2014 : The Twelfth Australasian Data Mining Conference, 27-28 November 2014, Queensland University of Technology, Brisbane, QLD.

Abstract

Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

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ID Code: 81787
Item Type: Conference Paper
Refereed: Yes
Keywords: multidimensional data, neighbourhood, dimensionality reduction, collaborative filtering, recommender systems
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright © 2014, Australian Computer Society, Inc.
Copyright Statement: This
paper appeared at Australasian Data Mining Conference
(AusDM 2014), Brisbane, 27-28 November 2014. Conferences
in Research and Practice in Information Technology, Vol. 158.
Richi Nayak, Xue Li, Lin Liu, Kok-Leong Ong, Yanchang
Zhao, Paul Kennedy Eds. Reproduction for academic, not-for
profit purposes permitted provided this text is included
Deposited On: 12 Feb 2015 23:40
Last Modified: 30 Mar 2016 07:02

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