A Network-Based Model for High-Dimensional Information Filtering

The Vector Space Model has been and to a great extent still is the de facto choice for profile representation in content-based Information Filtering. However, user profiles represented as weighted keyword vectors have inherent dimensionality problems. As the number of profile keywords increases, the vector representation becomes ambiguous, due to the exponential increase in the volume of the vector space and in the number of possible keyword combinations. We argue that the complexity and dynamics of Information Filtering require user profile representations which are resilient and resistant to this "curse of dimensionality". A user profile has to be able to incorporate many features and to adapt to a variety of interest changes. We propose an alternative, network-based profile representation that meets these challenging requirements. Experiments show that the network profile representation can more effectively capture additional information about a user's interests and thus achieve significant performance improvements over a vector-based representation comprising the same weighted keywords.