Multimodal recommendation systems increasingly adopt GNNs to perform message passing on the user–item interaction graph and content-based item graphs to learn user and item representations. However, existing methods that use static-graph GNNs in multimodal recommendation face two key limitations: (i) representation homogenization, where repeated aggregation on a fixed topology makes node embeddings overly similar and weakens personalized signals; and (ii) incomplete context modeling, where similarity-based item graphs mainly capture local pairwise resemblance but miss group- or scenario-level relations that are far apart in the similarity space. Together, these limitations reduce the quality of learned node representations on static graphs and lead to suboptimal recommendations. To alleviate these limitations, we propose ESG-Rec, a unified multi-view framework that constructs three complementary structural views: degree-aware structural diversification to preserve individualized collaborative signals, content-based semantic modeling to provide stable local relations, and group-level semantic modeling to capture higher-order contextual information. ESG-Rec further performs multi-view contrastive alignment to learn consistent representations across views. Extensive experiments on benchmark datasets demonstrate that ESG-Rec consistently improves recommendation performance over strong multimodal baselines and effectively mitigates the above two limitations.

Intelligent systems are technologically advanced machines that can sense and respond to the surrounding environment. They have been widely used in medicine, military, transportation, automation, and other fields. However, when these systems deal with their environments, problems such as leakage of identities may occur. The adversary can damage the system communication and attack important nodes. To handle resource-constrained wireless sensor network environments, we propose a secure and anonymous data aggregation scheme. First, based on the bilinear mapping operation and onion routing concepts, we propose a key negotiation and secure information transmission scheme, which conducts confidential transmission and anonymous forwarding of messages in data aggregation. Second, an aggregation routing scheme based on link direction and residual energy is proposed to pledge messages that can arrive the base station without passing through many nodes, which saves network resources to a certain extent. Third, on the basis of the first two contributions, we propose an identity-privacy-aware secure and anonymous data aggregation scheme that protects the identity's privacy. This scheme can conceal the real identity of important nodes and protect the anonymity of messages and link relationships. In addition, an anonymous identity update and synchronization scheme is also proposed to ensure the reliability and security of communication. Meanwhile, our performance evaluations and simulations show that the proposed framework is more effective than several standard schemes with respect to the ability against various attacks, security, and overhead.

Cross-domain sequential recommendation is the task of predict the next item that the user is most likely to interact with based on past sequential behavior from multiple domains. One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains. Previous studies have investigated the flow of behavioral information by exploring the connection between items from different domains. The flow of knowledge (i.e., the connection between knowledge from different domains) has so far been neglected. In this article, we propose a mixed information flow network for cross-domain sequential recommendation to consider both the flow of behavioral information and the flow of knowledge by incorporating a behavior transfer unit and a knowledge transfer unit . The proposed mixed information flow network is able to decide when cross-domain information should be used and, if so, which cross-domain information should be used to enrich the sequence representation according to users’ current preferences. Extensive experiments conducted on four e-commerce datasets demonstrate that the proposed mixed information flow network is able to improve recommendation performance in different domains by modeling mixed information flow. In this article, we focus on the application of mixed information flow network s to a scenario with two domains, but the method can easily be extended to multiple domains.