Towards an Intuitionistic Fuzzy Agglomerative Hierarchical Clustering Algorithm for Music Recommendation in Folksonomy

Chun Guan, Kevin Kam Fung Yuen, Frans Coenen

Abstract: Folksonomy, a system for social tagging or collaborative tagging, is popular in Semantic Web research. Folksonomy is applied to items, such as music pieces, which their personalized tags can be annotated by users. Recommendation systems can use these tags to produce meaningful information. Clustering methods, such as the Agglomerative Hierarchical Clustering (AHC) method, can be applied in the context of recommendation system. This paper proposes the Intuitionistic Fuzzy Agglomerative Hierarchical Clustering (IFAHC) algorithm for recommendation using social tagging. The Intuitionistic Fuzzy Set (IFS) concept is used to represent tag values which are vague and uncertain. IFAHC can cluster items represented by using IFS into different groups. The application of IFAHC to music recommendation is used to demonstrate the usability of the proposed method.