Social recommendation often incorporates trusted social links with user-item interactions to enhance rating prediction. Although methods that aggregate explicit social links have shown promising prospects, they are often constrained by the absence of explicit social data and the assumption of homogeneity, thus overlooking variations in social influence and consistency. These limitations hinder semantic expression and recommendation performance. Therefore, we propose a novel framework for social recommendation. First, we design a bipartite network embedding scheme, which learns vertex representations in the embedding space by modeling 1st-order explicit relations and higher-order implicit relations between vertices. Then, the similarity of the embedding vectors is used to extract top-k semantically consistent friends for each user. Next, we design an algorithm to assign a specific influence value to each user. Finally, we combine the top-k friends of the user and their influence values into an ensemble and add it as a regularization term to the rating prediction process of the user to correct the bias. Experiments on three real benchmark datasets show significant improvements in EISF over state-of-the-art methods.

In sequential recommendation, it is critical to accurately capture the user’s intention with limited session information. Previous work concentrates on modeling a single relationship existing between items in an ongoing session, e.g., sequential dependency or graph structures. They lack the exploration of constituent semantic properties of user intention and also lack effective mechanisms binding important features to deduce user intention accurately. In this paper, we present a novel intention detection-enhanced sequential recommendation model, named DASR, which can capture both sequential dependencies and user intent components. We innovatively introduce slot attention to bind the low-level local features extracted by CNN and design multiple rounds of competitive iteration mechanisms to refine the high-level representation of user intent continuously. Finally, these high-level features cooperate with the global dependencies captured by self-attention to achieve sequential recommendations. Extensive experiments are carried out on three benchmark datasets, the experimental results show that DASR outperforms the state-of-the-art baseline methods up to 6.91%, and 4.73% on Recall@20, and MRR@20, respectively.

Multimodal recommendation has become a promising paradigm for building advanced recommendation systems, enabling it to capture the user’s preferences accurately through an integration between the user’s collaborative signals and item modalities (e.g., text, vision, etc.). Current methods typically treat modalities as static supplements on item side and infer user preference jointly with user ID-based collaborative signals. Nevertheless, there are still two limitations: (1) The shared user ID embedding scheme forces the uncontrollable entanglement between the collaborative signal and the multimodal content on the latent space, failing to decouple historical preferences from content semantics when representing user preferences; (2) Data sparsity limits the model’s ability to capture the user’s global interest. Despite the introduction of original social relations as a complementary strategy, the inherent noise problem limits the robustness of the model. To overcome these limitations, we propose a novel Social Graph diffusion and Decoupled representation learning for Multimodal Recommendation (SGDMR). Specifically, it uses a decoupled graph propagation mechanism to separate the cooperative signal from the multimodal features, thereby eliminating the entanglement. Furthermore, to mitigate the adverse effects of noise on the model’s robustness, we introduce a social graph diffusion model to learn robust social structures. Additionally, we enhance local interactions and global semantic consistency through multi-task self-supervised learning. We conduct comprehensive comparison experiments against other state-of-the-art methods on three real-world datasets. The results demonstrate the superiority of SGDMR.