A DeepWalk-Based Approach to Defend Profile Injection Attack in Recommendation System

Abstract

In the open social networks, the analysis of user data after the injection attack has a great impact on the recommendation system. K-Nearest Neighborbased collaborative filtering algorithms are very vulnerable to this attack. Another recommendation algorithm based on probabilistic latent semantic analysis has relatively accurate recommendation, but it is not very stable and robust against attacks on the overall user data of the recommendation system. In this paper, we propose to use to DeepWalk the user network processing, while taking advantage of the user profile feature time series to consider the user's behavior over time, the algorithm also analyzes the stability and robustness of DeepWalk and user profile. The results show that especially the DeepWalk-based approach can achieve comparable recommendation accuracy.