英语翻译等我上传了照片再翻译,1

英语翻译
等我上传了照片再翻译,
1

The work in this paper covers the following three aspects:
(1) In view of information of users’ social relationships on the social network,it firstly made the modeling of the social network,then it proposed the algorithm for node similarity on social network in the method of learning with machine.In this algorithm,it uses the information of the nodes themselves,the computed results with the traditional node similarity algorithm and some auxiliary data as the characteristics of the training in learning with machine,and takes the edges (if any) between modes as the labeling objects,then uses the Logistic regression model as the training model to compute the node similarity.After computing the node similarity,it is proposed in this paper a algorithm with the node similarity data and the users’ scoring data to predict the users’ scoring,which is referred to as the users’ credit scoring in this paper.
(2) In view of problems as excessive time and space occupations faced in the mass users recommending system in the collaborative filtering recommendation algorithm which leads to failure of making recommendation for new users,it is proposed in this paper an improved collaborative filtering recommendation algorithm which firstly builds a “users’ characteristics – Item” scoring matrix,then solves the matrix with the latent semantic model,and finally,on the basis of the solved matrix and users’ characteristic data,predicts users’ scorings on items by means of linear weightings,which is generally referred to as the users’ preferential scoring in this paper.
(3) After computation of the users’ credit scoring and preferential scoring,the two scorings need to be inosculated so as to integrate a final users’ scoring on a certain item with a proper method.It is proposed in this paper a algorithm of inosculating the users’ credit scoring and preferential scoring in learning with machine,which uses the user’s credit scoring,preferential scoring,number of neighbors on the social network and number of characteristics as the characteristics of training,and takes the user’s actual scoring as the labeling objects,then uses the Fisher linear discrimination as the training model to predict user’s scoring on certain items.The scoring predicted with this algorithm is the user’s final scoring on items.