The performance of the Graph-based Semi-Supervised Learning (GSSL) method based on one graph mainly depends on a well-structured single graph and most algorithms based on multiple graphs are difficult to be applied while the data has only single view. Aiming at the issue, a Graph Transduction via Alternating Minimization method based on Multi-Graph (MG-GTAM) was proposed. Firstly, using different graph construction parameters, multiple graphs were constructed from data with one single view to represent data point relation. Secondly,the most confident unlabeled examples were chosen for pseudo label assignment through the integration of a plurality of map information and imposed higher weights to the most relevant graphs based on alternating optimization,which optimized agreement and smoothness of prediction function over multiple graphs. Finally, more accurate labels were given over the entire unlabeled examples by combining the predictions of all individual graphs. Compared with the classical algorithms of Local and Global Consistency (LGC), Gaussian Fields and Harmonic Functions (GFHF), Graph Transduction via Alternation Minimization (GTAM), Combined Graph Laplacian (CGL), the classification error rates of MG-GTAM decrease on data sets of COIL20 and NEC Animal. The experimental results show that the proposed method can efficiently represent data point relation with multiple graphs, and has lower classification error rate.

To address the difficulty of data sparsity and lower recommendation precision in the traditional Collaborative Filtering (CF) recommendation algorithm, a new CF recommendation method of integrating social tags and users background information was proposed in this paper. Firstly, the similarities of different social tags and different users background information were calculated respectively. Secondly, the similarities of different users ratings were calculated. Finally, these three similarities were integrated to generate the integrated similarity between users and undertook the recommendations about items for target users. The experimental results show that, compared with the traditional CF recommendation algorithm, the Mean Absolute Error (MAE) of the proposed algorithm respectively reduces by 16% and 22.6% in the normal dataset and cold-start dataset. The new method can not only improve the accuracy of recommendation algorithm, but also solve the problems of data sparsity and cold-start.