On social semantic relations for recommending tags and resources using folksonomies

Social tagging is an innovative and powerful mechanism introduced by social Web: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. However, due to the lack of rules for managing the tagging process and of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications do not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing; for this reason the most recent research in this area is dedicated to empower the social perspective applying semantic approaches in order to support tagging, browsing, searching, and adaptive personalization in innovative recommender systems. This paper proposes a survey on existing recommender systems, discussing how they extract social semantic relations (i.e. relations among users, resources and tags of a folksonomy), and how they utilize this knowledge for recommending tags and resources.