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

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. Clusterin...

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Bibliographic Details
Published in:	2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 2039 - 2042
Main Authors:	Chun Guan, Yuen, Kevin Kam Fung, Coenen, Frans
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.10.2015
Subjects:	Agglomerative Hierarchical Clustering Clustering algorithms Computer science Folksonomy Fuzzy sets Intuitionistic Fuzzy Set Metals Music Recommendation Recommender systems Semantic Web Tagging
Online Access:	Get full text
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Summary:	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.
DOI:	10.1109/SMC.2015.356