Abstract
Social tagging systems (STSs) allow users to annotate information items (songs, pictures, etc.) to provide them item/tag or even user recommendations. STSs consist of three main types of entities: 1) users; 2) items; and 3) tags. These data usually are represented by a three-order tensor, on which Tucker decomposition (TD) models are performed, such as higher order singular value decomposition. However, TD models require cubic computations for the tensor decomposition. Furthermore, TD models suffer from sparsity that incurs in social tagging data. Thus, TD models have limited applicability to large-scale datasets, due to their computational complexity and data sparsity. In this paper, we use two different ways to compute similarity/distance between tags (i.e., the term frequency - inverse document frequency vector space model and the semantic similarity of tags using the ontology of WordNet). Moreover, to reduce the size of the tensor's dimensions and its data sparsity, we use clustering methods (i.e., ${k}$ -means, spectral clustering, etc.) for discovering tag clusters, which are the intermediaries between a user's profile and items. Thus, instead of inserting the tag dimension in the tensor, we insert the tag cluster dimension, which is smaller and has less noise, resulting to better item recommendation accuracy. We perform experimental comparison of the proposed method against a state-of-the-art item recommendation algorithm with two real datasets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness and efficiency.