Social network recommendation system based on PPIN

To improve the performance of the social network recommendation system on massive data, a competition-inhibition node model (CINM) is proposed by combing the traditional clustering methods with the new features of the protein networks. The whole processing flow is divided into four parts including node reconstruction, out-of-band clustering, intra-film clustering and content recommendation, in which data preprocessing, data cleaning, precision matching and data output are performed, respectively. In data preprocessing, the user information with the complex cube is converted into the structured quantitative data by the matrix operation, and the data summary is generated. In data cleaning, the user's characteristic data are obtained by judging the competition value. During the precision matching phase, a set of values with the greatest similarity are acquired by the similarity matching principle of the protein-protein interaction network. The final content or the relationship can be recommended by the user-association data items. The experimental results show that the CINM model can complete data filtering by data preprocessing and eigenvalue competition prefabrication mechanism to improve the efficiency of data processing and the accuracy of the final recommendation results. © 2017, Editorial Department of Journal of Southeast University. All right reserved.