Development and Replication of Improved Recommendation Algorithm for Network Representation Learning

Abstract

With the rapid development of various Internet services, it has become easier to obtain auxiliary information. The application of such auxiliary information to the recommendation algorithm will improve the recommendation performance, but it also brings new ideas to the modeling ability of the recommendation algorithm. Based on the DeepWalk algorithm, this paper proposes a network representation learning recommendation algorithm based on a random walk. The number of wandering sequences is determined according to the importance of the nodes, and the termination probability is set so that the lengths of the wandering sequences are not the same, which is closer to the real situation. At the same time, in the process of node representation learning, the attribute information of the node is merged, the weight of the attribute information of the node is adaptively adjusted, and the distance between the context node and the central node is considered to obtain more accurate recommendation results. Experimental results on 3 data sets show that the algorithm has good recommendation performance and effectively solves the cold start problem.