Research on E-Commerce Network Link Prediction Based on Improved DeepWalk Algorithm

[Objective] This paper aims to address the deficiency of DeepWalk algorithm in link prediction of e-commerce network, a link prediction algorithm based on improved DeepWalk algorithm is proposed.[Methods] According to the problem that the traditional DeepWalk algorithm treats each node equally in the random walk process, the structure and attribute information of the e-commerce network are biased to the random walk, so as to guide the walking process to traverse different types of nodes in the graph more targeted.This paper solves the problem that the traditional DeepWalk algorithm can not well represent the relationship between users and commodities by using cosine similarity measurement method, Bhattacharyya Coefficient is introduced into the existing nonlinear similarity calculation model to create a new similarity model.[Results] Experimental results show that the average recall accuracy of the improved DeepWalk algorithm is improved by a maximum of 0.17 and a minimum of 0.05 in different data scales.When calculating the node similarity, the optimal value of the node attribute similarity contribution alpha is between 0.5 and 0.6.[Limitations] In this paper, sensitivity parameters must be set subjectively, and the time complexity of the random walk is proportional to the network size N, so the scalability of the algorithm may decrease as the time complexity increases.[Conclusions] It shows that the improved algorithm can learn the node embedding vector well, so as to understand the similarity of nodes in the e-commerce network. © 2024 Chinese Academy of Sciences. All rights reserved.