COLLABORATIVE RECOMMENDATION SYSTEMS BASED ON SEMI-SUPERVISED FUZZY CLUSTERING METHOD AND APPLING IN CO-AUTHOR NETWORKS

Bui The Hong

Bui The Hong Hung Yen University of Technology and Education

Abstract

The collaborative recommendation problem among researchers is currently being emphasized. Most of the existing reseaches deal with collaborative recommendation problems based on collaborative and non-collaborative binary classification. However, due to the sparseness of the co-authors network, the data set used for training is often subject to imbalance leading to low classification efficiency. This paper proposes a collaboration recommendation system based on a fuzzy semi-supervised clustering to overcome the disadvantages of binary clustering for sparse and unbalanced data. Experimental results for the proposed collaborative recommendation system were empirically tested on a practical data set, suggesting that in most cases a more effective fuzzy semi-observer clustering collaboration recommendations system would be more effective compared with the binary classification system.

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