Learning user preference patterns for Top-N recommendations Ren,Y, Li,G and Zhou,W 2012, Learning user preference patterns for Top-N recommendations, in IEEE/WIC/ACM 2012 : Proceedings from the International Conference on International Conferences on Web Intelligence and Intelligent Agent Technology, IEEE, Piscataway, N.J., pp. 137-144, doi: 10.1109/WI-IAT.2012.102. Attached Files Name Description MIMEType Size Downloads Title Learning user preference patterns for Top-N recommendations Author(s) Ren,Y Li,G orcid.org/0000-0003-1583-641X Zhou,W orcid.org/0000-0002-1680-2521 Conference name Web Intelligence and Intelligent Agent Technology. Joint Conferences (2012 : Macau, China) Conference location Macau, China Conference dates 4-7 Dec. 2012 Title of proceedings IEEE/WIC/ACM 2012 : Proceedings from the International Conference on International Conferences on Web Intelligence and Intelligent Agent Technology Editor(s) [Unknown] Publication date 2012 Conference series Web Intelligence and Intelligent Agent Technology Joint Conferences Start page 137 End page 144 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Pattern Recognition Top-N recommendations Summary In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE. ISBN 9780769548807 Language eng DOI 10.1109/WI-IAT.2012.102 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2012, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30067478 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 14 Nov 2014, 14:51:21 EST Fri, 14 Nov 2014, 14:57:17 EST Sat, 15 Nov 2014, 04:00:54 EST Mon, 01 Jun 2015, 10:55:05 EST Mon, 01 Jun 2015, 10:56:01 EST Mon, 08 Jun 2015, 11:33:26 EST Mon, 12 Oct 2015, 15:38:55 EST Mon, 04 Dec 2017, 13:27:52 EST Mon, 04 Dec 2017, 13:29:24 EST Mon, 04 Dec 2017, 13:30:13 EST Sat, 17 Feb 2018, 01:30:19 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 281 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 14 Nov 2014, 14:57:17 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.