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With more and more information available on the Internet, the task of making personalized recommendations to assist the user's navigation has become increasingly important. Considering there might be millions of users with different backgrounds accessing a Web site everyday, it is infeasible to build a separate recommendation system for each user. To address this problem, clustering techniques can first be employed to discover user groups. Then, user navigation patterns for each group can be discovered, to allow the adaptation of a Web site to the interest of each individual group. In this paper, we propose to model user access sequences as stochastic processes, and a mixture of Markov models based approach is taken to cluster users and to capture the sequential relationships inherent in user access histories. Several important issues that arise in constructing the Markov models are also addressed. The first issue lies in the complexity of the mixture of Markov models. To improve the efficiency of building/maintaining the mixture of Markov models, we develop a lightweight adapt-ive algorithm to update the model parameters without recomputing model parameters from scratch. The second issue concerns the proper selection of training data for building the mixture of Markov models. We investigate two different training data selection strategies and perform extensive experiments to compare their effectiveness on a real dataset that is generated by a Web-based knowledge management system, Livelink. [ABSTRACT FROM AUTHOR]

Structural computing represents a form of hypertext, in which structure is foregrounded in the analysis, design and implementation of systems. Until now, the vast majority of structural computing work has focused on the implementation of frameworks. The applications implemented in these environments are often reimplementations of existing hypertext applications, intended to show the utility of the provided environments in terms of implementation, interoperability, or other metrics. Often, spatial hypertext applications are reimplemented, since they are seen as particularly good tests of structural computing infrastructures. This is because the demonds they pose are quite different from many other types of hypertext applications. However, structural computing researchers have failed to reanalyse and redesign these applications in a structurally-aware way. There are substantial improvements that result from rethinking such applications from a structure-first perspective. In this paper, we present such a reanalysis, and describe its implications for spatial hypertext within structural computing environments. [ABSTRACT FROM AUTHOR]