Abstract
Social Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, web links, products etc.). Social tagging systems (STSs) can recommend users with common social interest based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a model to capture the three types of entities that exist in a social tagging system: users, items, and tags. These data are represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) method. We perform experimental comparison of the proposed method against a baseline user recommendation algorithm with a real data set (BibSonomy), attaining significant improvements.