Unified collaborative filtering model based on combination of latent features

Zhong, JA and Li, X (2010) Unified collaborative filtering model based on combination of latent features. Expert Systems with Applications, 37 8: 5666-5672. doi:10.1016/j.eswa.2010.02.044

Author Zhong, JA
Li, X
Title Unified collaborative filtering model based on combination of latent features
Journal name Expert Systems with Applications   Check publisher's open access policy
ISSN 0957-4174
Publication date 2010-08
Sub-type Article (original research)
DOI 10.1016/j.eswa.2010.02.044
Volume 37
Issue 8
Start page 5666
End page 5672
Total pages 7
Place of publication Oxford, United Kingdom
Publisher Pergamon
Collection year 2011
Language eng
Abstract Collaborative filtering (CF) has been studied extensively in the literature and is demonstrated successfully in many different types of personalized recommender systems. In this paper, we propose a unified method combining the latent and external features of users and items for accurate recommendation. A mapping scheme for collaborative filtering problem to text analysis problem is introduced, and the probabilistic latent semantic analysis was used to calculate the latent features based on the historical rating data. The main advantages of this technique over standard memory-based methods are the higher accuracy, constant time prediction, and an explicit and compact model representation. The experimental evaluation shows that substantial improvements in accuracy over existing methods can be obtained. © 2010 Elsevier Ltd. All rights reserved.
Keyword Recommender System
Collaborative filtering
Probabilistic latent semantic analysis
Latent feature
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 14 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 22 times in Scopus Article | Citations
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Created: Sun, 20 Jun 2010, 00:06:15 EST