Revisiting Evolutionary Information Filtering

Adaptive Information Filtering seeks a solution to the problem of information overload through a tailored representation of the user's interests, called user profile, which constantly adapts to changes in them. Evolutionary Algorithms have been proposed as a solution to the problem of profile adaptation, but the relevant attempts have not produced successful real world applications. In this paper, we argue that Adaptive Information Filtering is a complex and dynamic problem not easily addressed with Genetic Algorithms and Memetic Algorithms that adopt weighted keyword vector for profile representation. We discuss the theoretical issues and provide experimental evidence showing that such an approach suffers due to the large number of dimensions in the underlying vector space. The genetic operators cannot randomly produce the right combinations of keyword weights, given the very large number of possible combinations. With the current work, we wish to reanimate the interest in Evolutionary Information Filtering. Profile adaptation is a challenging problem with no established solution and biologically inspired solutions have still an important role to play in solving it. This however requires experimental methodologies that reflect the complexity and dynamics of the problem. This paper is part of ongoing research work in this direction.