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Collaborative recommendation is the problem of analyzing the content of an information retrieval system and actions of its users, to predict additional topics or products a new user may find useful. Developing this capability poses several challenges to machine learning and reasoning under uncertainty. Recent systems such as CiteSeer [1] have succeeded in providing some specialized but comprehensive indices of full documents, but the kind of probabilistic models used in such indexing do not extend easily to information Grid databases and computational Grid workflows. The collection of user data from Grid portals [4] provides a test bed for the underlying IR technology, including learning and inference systems. To model workflows created using the TAVERNA editor [3] and SCUFL description language, the DESCRIBER system, shown in Figure 1, applies score-based structure learning algorithms, including Bayesian model selection and greedy search (cf. the K2 algorithm) adapted to relational graphical models. Figure 2 illustrates how the decision support front-end of DESCRIBER interacts with modules that learn and reason using probabilistic relational models,. The purpose is to discover interrelationships among, and thereby recommend, components used in workflows developed by other Grid users.

Relational Graphical Models of Computational Workflows for Data Mining