Building user communities of interests by using latent semantic analysis

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Xu, Guandong (2011) Building user communities of interests by using latent semantic analysis. In: Collaborative search and communities of interest: trends in knowledge sharing and assessment. Francq, Pascal, ed. Premier reference source . Information Science Reference, Hershey, Pa., pp. 38-68.

Abstract

Nowadays Web users are facing the problems of information overload and drowning due to the significant and rapid growth in the amount of information and the large number of users. As a result, how to provide Web users more exactly needed information is becoming a critical issue in Web-based information retrieval and data management. In order to address the above difficulties, Web mining was proposed as an efficient means to discover the intrinsic relationships among Web data. In particular, Web usage mining is to discover Web usage patterns and utilize the discovered usage knowledge for constructing interest-oriented user communities, which could be, in turn, used for presenting Web users more personalized Web contents, i.e. Web recommendation. On the other hand, Latent Semantic Analysis (LSA) is one kind of approaches that is used to reveal the inherent correlation resided in co-occurrence activities, such as Web usage data. Moreover, LSA possesses the capability of capturing the hidden knowledge at semantic level that can’t be achieved by traditional methods. In this chapter, we aim to address building user communities of interests via combining Web usage mining and latent semantic analysis. Meanwhile we also present the application of user communities for Web recommendation.

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/7610
DOI https://doi.org/10.4018/978-1-61520-841-8.ch004
ISBN 9781615208418
Subjects Historical > FOR Classification > 0807 Library and Information Studies
Historical > SEO Classification > 8902 Computer Software and Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID22848, Web usage, modelling user, Web data mining, Internet, semantics
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