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