Modelling User Behaviour for Web Recommendation Using LDA Model

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Xu, Guandong, 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, Pasi, Gabriella, Zhang, Chengqi, Cercone, Nick and Cao, Longbing, eds. IEEE Computer Society, Los Alamitos, California, pp. 529-532.

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.

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/6128
DOI https://doi.org/10.1109/WIIAT.2008.313
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
ISBN 9780769534961
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > SEO Classification > 8903 Information Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
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
Citations in Scopus 32 - View on Scopus
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