Learning utility functions from preference relations on graphs
The increasing popularity of graphs as fundamental data structures, due to their inherent flexibility in modeling information and its structure, has led to the development of methods to efficiently store, search and query graphs. Graphs are nonetheless complex entities whose analysis is cognitively challenging. This calls for the development of decision support systems that build upon a measure of ‘usefulness’ of graphs. We address this problem by introducing and defining the concept of ‘graph utility’. As the direct specification of utility functions is itself a difficult problem, we explore the problem of learning utility functions for graphs on the basis of user preferences that are extracted from pairwise graph comparisons.
KeywordsUtility Function Recommendation System Marginal Utility Utility Model Query Graph
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