# Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios
###### Abstract  
One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2 vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2 vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split into two parts, namely, a static history portrait and a dynamic preference portrait; the NRH model adopts a hybrid recommendation approach based on collaborative filtering and a knowledge graph to obtain the user's preferences on three publicly accessible datasets; and comparison experiments with the mainstream model are lastly carried out. The AUC and ACC increased, respectively, by 0.9% to 29.5% and 1.6% to 31.4% in the MovieLens-1M dataset, by 1.5% to 17.1% and 4.4% to 18.7% in the Book-Crossing dataset, and by 0.8% to 27.9% and 2.9% to 24.1% in the LastFM dataset. The RippleNet model was used for comparison experiments comparing suggestion diversity. According to the experimental findings, the NRH model performs better in accuracy and variety than the popular customized knowledge graph recommendation algorithms now in use. RippleNet; preference propagation; hybrid recommendation; user portrait +
Footnote †: journal: Medical Informatics  
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Footnote 1: email: _Applied_  
In the conventional knowledge graph-based recommendation system, user portraits are modeled based on views and purchases made by users in the past. Utilizing the knowledge graph for preference propagation, the recommendation system generates suggestions based on the past behaviors of users and the entity relationships in the knowledge graph. Due to the potential limitations of knowledge representation in the knowledge graph, the recommendation system may be unable to exploit the global connections and characteristics among items. The interests and preferences of users are subject to change over time. When user interests change, if the recommendation system fails to update the user portrait promptly, localization of preference propagation will become a problem. The knowledge graph-based recommendation system tends to classify User A into the interest group of comedy movies based on their past behaviors and the association in the knowledge graph. Furthermore, the system is more likely to associate them with comedy movies. However, if User A's interest gradually shifts to suspense films, since the knowledge graph fails to discover the association between comedy movies and suspense movies promptly, resulting in the recommendation system failing to update the user portrait, the recommendation system continues to recommend comedy movies to User A despite User A's current interest. Therefore, the recommendation result is biased toward comedic films and needs more diversity and customization. This paper introduces a hybrid recommendation model NRH (Node2 vec-side and Ripplenet Hybrid Model) for personalized recommendation scenarios. The optimized model can overcome the problem of localization of preference propagation in the process of user image updating by the RippleNet [8] model and improve the \"information cocoon\". To build item portraits and fully utilize the association relationship between items, the NRH model uses Node2 vec-side knowledge representation. This approach is suitable for large-scale personalized recommendation scenarios. The NRH model also creates a static historical image of users based on information about user-item history interaction before optimizing the RippleNet model for preference propagation. We adopt a hybrid prediction method based on collaborative filtering and a knowledge graph to make recommendations.