Social media filtering based on collaborative tagging in semantic space

We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues.