SFNN: A secure and diverse recommender system through graph neural network and regularized variational autoencoder

Recommender systems are frequently improved to filter information and provide users with the most relevant items. However, they face limitations in balancing appropriate and diverse recommendations while ensuring the security and integrity of user data. A new recommender system based on secure fusion neural network is pre sented in this paper. It guarantees data integrity and confidentiality while balancing accuracy and diversity. It integrates a graph neural network that models user-item interactions to improve accuracy, with a regularized variational autoencoder whose evidence lower bound loss function is enhanced by a diversity-promoting regu larization term that favors latent-space dispersion, thereby improving recommendation diversity. To optimize the combination of the two neural networks scores, an adaptive fusion mechanism is introduced to generate final predictions that consider diverse user preferences while maintaining relevance. Furthermore, our approach uses blockchain technology to encrypt and secure data storage, ensuring the integrity and confidentiality of users’ data. The experiments conducted on three datasets show that the proposed model can achieve an accuracy of 78.13 % with an intra-list diversity of 46.82 % for Retail Rocket dataset, an accuracy of 82.44 % with an intra-list diversity of 37.78 % for clothing dataset, and an accuracy of 86.16 % with an intra-list diversity of 47.65 % for MovieLens-1 M dataset.