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Modeling user hidden navigational behavior for web recommendation

Xu, Guandong and Li, Lin and Zhang, Yanchun and Yi, Xun and Kitsuregawa, Masaru (2011) Modeling user hidden navigational behavior for web recommendation. Web Intelligence and Agent Systems, 9 (3). pp. 239-255. ISSN 1570-1263

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Abstract

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analyses of Web usage data will lead to discovering Web user access patterns, and in turn, facilitating users to locate more preferable Web contents via collaborative recommendation techniques. In the context of Web usage mining, Latent Semantic Analysis (LSA) based on probability inference provides a promising approach to capture not only user hidden navigational patterns, but also the associations between users, pages and hidden navigational patterns. The discovered user access patterns could be used as a usage reference base for identifying the new user’s access preferences and making usage-based collaborative Web recommendations. In this paper, we propose a novel usage-based Web recommendation framework, in which Latent Dirichlet Allocation (LDA) is employed to learn the underlying task space from the training Web log data and infer the task distribution for a target user via task inference. The main advantages of the adapted LDA model are its capabilities of efficiently learning the semantic usage information from the Web log data and effectively inferring the access preference of the target user even with a few Web clicks that might be unseen in the training data. In this paper, we also investigate the determination of an optimizing task number, which is another important problem commonly encountered in latent semantic analysis. Experiments conducted on a real Web log dataset show that this approach can achieve better recommendation performance in comparison to other existing techniques. And the discovered task-simplex expression can also provide a better interpretation for Web designers or users to better understand the user navigational preference.

Item Type: Article
Uncontrolled Keywords: ResPubID23053, ResPubID23612, user modeling, web recommendation, hidden navigational task mining, latent Dirichlet allocation
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0806 Information Systems
SEO Classification > 8902 Computer Software and Services
Faculty/School/Research Centre/Department > School of Engineering and Science
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Depositing User: VUIR
Date Deposited: 09 Aug 2012 05:03
Last Modified: 23 Sep 2014 03:33
URI: http://vuir.vu.edu.au/id/eprint/8923
DOI: 10.3233/WIA-2011-0217
ePrint Statistics: View download statistics for this item
Citations in Scopus: 2 - View on Scopus

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