基于二部图多权重投影的大数据推荐算法

基于网络结构的推荐算法存在多样性不足的问题，为此提出了一种基于二部图多权重投影的大数据推荐算法。首先，提取出数据集的基础信息，将所有的项目—用户数据输入莱文斯坦距离程序，计算各个属性之间的相似性；然后，计算二部图网络中节点之间相同邻居的数量、节点之间的共同邻居度以及每个节点的度，计算二部图网络中每条边的三重权重；最后，采用增强的二部图投影技术提取二部图网络的潜在链接，实现基于相似性的链接预测。在大数据集与小数据集上分别开展了实验，结果显示该算法的准确率与覆盖率均优于其他几种类型的推荐算法，并且优于同类型的推荐算法。

Recommendation system of big data based on multi-weight projection of bipartite network

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, the basic information of datasets was abstracted, items-users lists were applied as an input to Levenshtein distance algorithm to compute similarity of each property; Then, 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 were all computed, triple weights of each side of the bipartite network were computed; Lastly, the enhanced bipartite projection technique was adopted to abstract the potential links of the bipartite network to realize the link prediction based on similarity. The experiments based on both of big dataset and small dataset were realized, 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.