Granular Semantic User Similarity in the Presence of Sparse Data

Finding similar users in social communities is often challenging, especially in the presence of sparse data or when working with heterogeneous or specialized domains. When computing semantic similarity among users it is desirable to have a measure which allows to compare users w.r.t. any concept in the domain. We propose such a technique which reduces the problems caused by data sparsity, especially in the cold start phase, and enables granular and contextbased adaptive suggestions. It allows referring to a certain set of most similar users in relation to a particular concept when a user needs suggestions about a certain topic (e.g. cultural events) and to a possibly completely different set when the user is interested in another topic (e.g. sport events). Our approach first uses a variation of the spreading activation technique to propagate the users’ interests on their corresponding ontology-based user models, and then computes the concept-biased cosine similarity (CBC similarity), a variation of the cosine similarity designed for privileging a particular concept in an ontology. CBC similarity can be used in many adaptation techniques to improve suggestions to users. We include an empirical evaluation on a collaborative filtering algorithm, showing that the CBC similarity works better than the cosine similarity when dealing with sparse data.