With the emergence of new and transformative ways of creating connection, collaboration, and sharing on the Internet, the phenomenon of rich social behavior on the Internet has attracted the attention of researchers and practitioners. In recent years, with the popularity and promotion of social network platforms, recommendation systems based on social networks have also become one of the research hotspots in the field of personalized recommendation. Social recommendation systems use social networks to alleviate the data sparsity problem in traditional recommendation algorithms. In social networking, social relationships have played an important role, and user trust is the foundation of social relationship. Every user is affected by the users they trust. These trusted users will be affected by their own social relations, which means that users associated with each and there is preference similarity among socially connected users. The user's trust relationship affects the user's preference, and the user is affected by the social relationship between its trust users, and these social relationships are increasing and spreading in social networks. Therefore, the focus of social recommendation algorithm researchlies in the mining and utilization of trust informationin the field of recommendation based on social network, a representative model is DiffiNet, which has not fully considered the problem of trust, and at the same time, there is additional noise when recursive long-distance social relationships, affecting the quality of the recommended forecasts. Therefore, a DIFFNET improved social recommendation model-EIDNetis proposed in this paper. Firstly,when simulating the diffusion process of social relationship influence, the trust relationship between usersisestablishcdby the historical interaction records of users with items, and integrates it into the recursive social dynamic modeling to obtain different trust relationships for different users for different items. Second, Secondly, when calculating social influence, a method of increasing residual connections is proposed to reduce the noise generated by long-distance social relationships. At the same time, in order to solve the problem of weight distribution in the same order domain, an attention mechanism is proposed to learn the user friend relationship vector, calculate different weights for different users, and adaptively measure the social influence among users' friends. Ultimately, the three parts arc fused into a unified framework to reinforce each other and build a better scaling model. Finally, the user's future behavior and preferences are predicted by combining the user's historical interaction behavior and social relationship with the item. The main contributions of this paper include: (l)Integrate trust based on historical interaction records between users and items into recursive social dynamic modeling to solve the problem of different users recommending different items due to trust relationships; (2) In the recursive calculation of long-distance social relations, a residual connection method is proposed to reduce the influence of noise; (3) Attention mechanism is introduced tomatch different importance to users in the social network to solve the problem of weight distribution in the same order domain. The experimental results show that, compared with the best performance results of DiffNct, the performance of EIDNct on Yelp is improved by 10. 61%, and the performance on Flickr is improved by 24. 98%. The results confirm that the improved model proposed in this paper improves the recommendation performance of the social recommendation model. © 2023 Science Press. All rights reserved.
