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Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes . First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possib ility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is simila rity, which indicates the tendency of keywords to co-occur ; and the other is mutual exclusion, which describes the tendency of k eywords to exclude each othe r. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to con struct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]

Network node prediction research currently focuses on the prediction of source nodes and hidden nodes, but lacks research on prediction of new nodes. This paper took the relational network of papers and keywords as the research object, used keyword combination to predict the emergence of new papers, and carried out the prediction research of new nodes. First, this essay projected and weighted the paper-keyword bipartite network into a keyword relational network, and then used the possibility of keyword combination to predict the emergence of new papers in the future. There are two aspects to consider to calculate this possibility. One is similarity, which indicates the tendency of keywords to co-occur; and the other is mutual exclusion, which describes the tendency of keywords to exclude each other. For example, two keywords with a high degree of concord rarely appear at the same time. Collected the papers and keywords information of the journal to construct the dataset, it verifies the proposed new paper prediction algorithm, and compared with the existing algorithms . The results show that the node prediction algorithm proposed has better prediction effect. [ABSTRACT FROM AUTHOR]

Neighborhood-based top-N recommendation techniques for implicit users ' inte raction data are ranking models, of which similarity functions are the important ingredients. Traditional similarity functions suffer from two major issues: sparse data and high dimensional data. Sparse data hinder the recommendation system from scoring on smooth neighborhood, high dimensional data causes the curse of dimensionality problem. This paper proposed a representation-based approach named multi object representation learning ( MO) for recommendation, MO was a bipartite node representation learning algorithm, which embedded the different level of network structures and item ordering information into nodes representations to help leverage the recommendation performance. Experimental results on three real data sets of different scales show that the algorithm has higher accuracy and recall than the commonly used recommendation model based on implicit feedback, especially for large-scale data sets, which can effectively alleviate the problems of matrix sparsity and dimensionality disaster, and improve the recommended performance. [ABSTRACT FROM AUTHOR]

Most recommendation systems based on the network structure suffer from lack of diversity, so that the paper proposed a recommendation system of big data based on multi-weight projection of bipartite network. Firstly, it abstracted the basic information of datasets, and applied items-users lists as an input to Levenshtein distance algorithm to compute similarity of each property. Then, it computed the number of common neighbors of the nodes in the bipartite network, the degree of common neighbors of the nodes in the bipartite network and degree of each node in bipartite network, and also computed the triple weights of each side of the bipartite network. Lastly, it adopted the enhanced bipartite projection technique to abstract the potential links of the bipartite network to realize the link prediction based on similarity. It realized the experiments based on both of big dataset and small dataset. The results show that the proposed algorithm outperforms different kinds of recommendation systems in terms of accuracy and coverage of recommendation, at the same time, it outperforms the other recommendation system based on network structure. [ABSTRACT FROM AUTHOR]

The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system. [ABSTRACT FROM AUTHOR]