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Using Structural Content Information for Learning User Profiles

In this paper we show how a user profile (in the context of a content-based recommender system and represented by means of a Bayesian Network) can be enhanced when a more detailed description of an item’s content is considered. Two main assumptions have been considered: the first implies that the set of features used to describe an item can be organized into a well-defined set of components or categories, and the second is that the user’s rating for a given item is obtained by combining user opinions of the relevance of each component. This new user profile therefore consists of two different parts: one represents how the user should rate each content component, and the other represents how this information might be combined in order to obtain the overall rating. As users do not normally express their opinions about structural components, in this paper we propose that these components be considered as hidden variables. The user profile is learnt by means of the Expectation-Maximization algorithm from the set of user’s ratings.