| Citation: | NI W K，PENG S F，DU Y H. Identification of induced information for personalized recommendations based on knowledge graph[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（7）：2538-2552 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0475
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In the age of intelligent information, managing the recommendations of Internet information service algorithms is a crucial step in creating a national Internet governance framework. Personalized recommendation algorithm is one of the important technologies for Internet information service algorithm recommendation. The knowledge graph is widely used in personalized recommendation algorithms. At the same time, the knowledge graph and recommendation algorithm are vulnerable to data poisoning attacks by attackers, which in turn affects the recommendation results and induces information dissemination. There is a lack of effective models for identifying this type of induced information. Based on this, this article conducts research on the induced information identification model. Based on the analysis of user historical behavior records and the evolution process of user preferences, we study induction based on user interests and group perception. Information detection method, perform group preference modeling on the historical preferences of similar user groups, perform outlier analysis on abnormally exposed information within groups with common characteristics, and construct set node2vec-side item representation, GMM group division, and LUNAR induction for anomaly detection User preference modifications and recommendation result evolution reasoning are the basis for the realization of induced information recognition by the information recognition model NGL (Induced information detection model that incorporates node2vec-side item representation, GMM group division, and LUNAR, NGL). Induced information recognition experiments were conducted on RippleNet and MKR recommendation systems. The results show that the NGL model proposed in this article is better than the existing anomaly detection model.
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