Abstract
Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate (or annotate) items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes.