Search

To deal with the issues like existing common data sparseness in weibo social network and the phenomena of cold start, this paper puts forward a two-stage clustering based on the recommendation algorithm GCCR. The algorithm firstly selects users’ focused nodes which have higher number, so as to extract a dense subset of sparse data, and by using the method of graph paper, similar concerned interested core clustering is formed to this dense subset. Then, it is extracted that weibo content features of seed clustering and the whole data set other users. Then the entire user group is clustered based on content similarity. Finally the clustering results are used in subject recommendation. Through clustering the two phases of dense data subset and the whole data set, the clustering effect of extreme sparse data sets are improved. At the same time, because of fuzziness of graph clustering, this thesis retains a certain diversity in the process of user interest clustering, so as to avoid convergence too fast when cold start. This method is verified through the real social network data, and the experimental results show that this algorithm can effectively solve the problems such as data sparseness and cold start phenomenon.

To improve the search accuracy and diversity of non-dominated sorting genetic algorithm (NSGA-II), an improved algorithm DMNSGA-II referencing to the strategy of differential evolution to strengthen local search is proposed in this paper. The algorithm uses mutation guiding operator and crossover operator of DE to replace crossover operator in NSGA-II to enhance the local search capability and improve search accuracy. while retaining the mutation operator of NSGA-II to improve diversity. We use four benchmark test problems to investigate the performance of the DMNSGA-II algorithm, and simulation results demonstrate that the proposed algorithm can achieve a good overall performance in multi-objective optimization.