Research Repository

Modelling User Behaviour for Web Recommendation Using LDA Model

Xu, Guandong and Zhang, Yanchun and Yi, Xun (2008) Modelling User Behaviour for Web Recommendation Using LDA Model. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops WI-IAT Workshops 2008 : 9-12 December 2008 University of Technology, Sydney, Australia, proceedings. Li, Yuefeng and Pasi, Gabriella and Zhang, Chengqi and Cercone, Nick and Cao, Longbing, eds. IEEE Computer Society, Los Alamitos, California, pp. 529-532.

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Abstract

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference.

Item Type: Book Section
ISBN: 9780769534961
Uncontrolled Keywords: ResPubID14744, Internet, Web sites, behavioural sciences computing, boundary-value problems, groupware, information filters, probability, user modelling, Web usage data, Web users access pattern, collaborative recommending technique, latent semantic analysis techniques, probability inference, real Website usage dataset, Web recommendations, Web usage mining, collaboration, inference algorithms, intelligent agent, linear discriminant analysis, mathematical models, computer models, navigation, pattern analysis
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
SEO Classification > 8903 Information Services
Faculty/School/Research Centre/Department > School of Engineering and Science
Depositing User: VUIR
Date Deposited: 19 Nov 2013 05:27
Last Modified: 20 Nov 2013 03:11
URI: http://vuir.vu.edu.au/id/eprint/6128
DOI: 10.1109/WIIAT.2008.313
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Citations in Scopus: 6 - View on Scopus

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