Towards A Hybrid Approach of Primitive Cognitive Network Process and Agglomerative Hierarchical Clustering for Music Recommendation

Chun Guan; Kevin Kam Fung Yuen

11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Research Article

Towards A Hybrid Approach of Primitive Cognitive Network Process and Agglomerative Hierarchical Clustering for Music Recommendation

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@INPROCEEDINGS{10.4108/eai.19-8-2015.2261344,
    author={Chun Guan and Kevin Kam Fung Yuen},
    title={Towards A Hybrid Approach of Primitive Cognitive Network Process and Agglomerative Hierarchical Clustering for Music Recommendation},
    proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness},
    publisher={IEEE},
    proceedings_a={QSHINE},
    year={2015},
    month={9},
    keywords={primitive cognitive network process hierarchical clustering recommendation system},
    doi={10.4108/eai.19-8-2015.2261344}
}

Chun Guan
Kevin Kam Fung Yuen
Year: 2015
Towards A Hybrid Approach of Primitive Cognitive Network Process and Agglomerative Hierarchical Clustering for Music Recommendation
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2261344

Chun Guan¹, Kevin Kam Fung Yuen²^,*

1: University of Liverpool
2: Xi'an Jiaotong-Liverpool University

*Contact email: kevinkf.yuen@gmail.com

Abstract

Clustering algorithms have been used in many real world applications including recommendation systems. This paper proposes PCNP-AHC, which is a hybrid approach of Primitive Cognitive Network Process (PCNP) and Agglomerative Hierarchical Clustering (AHC) to cluster music pieces on the basis of userâs preferences and similarities between music pieces. PCNP is an ideal alternative of Analytic Hierarchy Process (AHP) to quantify weights of attributes which are used in clustering process. The application of PCNP-AHC for music recommendation is demonstrated.

Keywords: primitive cognitive network process hierarchical clustering recommendation system

Published: 2015-09-08
Publisher: IEEE

: http://dx.doi.org/10.4108/eai.19-8-2015.2261344