Learning Semantic User Profiles from Text

This paper focuses on the problem of choosing a representation of documents that can be suitable to induce more advanced semantic user profiles, in which concepts are used instead of keywords to represent user interests. We propose a method which integrates a word sense disambiguation algorithm based on the WordNet IS-A hierarchy, with two machine learning techniques to induce semantic user profiles, namely a relevance feedback method and a probabilistic one. The document representation proposed, that we called Bag-Of-Synsets improves the classic Bag-Of-Words approach, as shown by an extensive experimental session.