Abstract
Collaborative filtering based clustering has been proved to have many advantages. In this paper, a novel heterogenous dynamic evolutionary clustering is presented. Firstly, the items and users are regarded as heterogenous individuals in the network. According to the dynamic network model, they are clustered into several groups. Secondly, item-based collaborative filtering is adopted in each cluster. Similarity between individuals only in the same cluster are computed not for all individuals in the system. The target rating is calculated according to the item-based collaborative filtering in its cluster. Diverse simulations show the efficiency of our proposed methods. Moreover, the presented methods gain better prediction results than two existing better algorithms.
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Acknowledgment
This work is supported by National Natural Science Foundation of China (No. 11261034, 71561020, 61503203, 11326239); Higher school science and technology research project of Inner Mongolia (NJZY13119); Natural Science Foundation of Inner Mongolia (2015MS0103, 2014BS0105).
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Chen, J., Uliji, Wang, H., Zhao, C. (2016). Collaborative Rating Prediction Based on Dynamic Evolutionary Heterogeneous Clustering. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_48
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DOI: https://doi.org/10.1007/978-981-10-3614-9_48
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