Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.

Exploring user-item interaction cues is crucial for the performance of recommender systems. Explicit investigation of interaction cues is made possible by using graph-based models, where each user-item relationship is described by an edge, and the introduction of user-user social network. While existing graph-based recommendation methods use only a single-value edge to define the relationship between a pair of user and item, which limits the ability to represent complex user-item interactions. Furthermore, some social recommendation methods overlook the heterogeneous user behavior patterns in social and interaction relationships, resulting in the suboptimal performance of existing systems. In this paper, we propose a novel Social-aware Multi-dimensional Edge Feature-based Graph Representation Learning method, called SMEF. It represents all users and items as a graph and deep learns a multi-dimensional edge feature to explicitly describe the task-specific relationships of each user-item pair. Specifically, the proposed SMEF focuses on two distinct user behavior patterns toward social friends and interactive items, which explore the underlying heterogeneous relationship cues within them. This way, the learned multi-dimensional edge features encode user information from both social and interaction aspects. The proposed SMEF is a plug-and-play module that can be combined with different recommendation frameworks and Graph Neural Networks (GNNs) backbones to generate high quality user representations. The experimental results achieved on three publicly accessible datasets show that our SMEF-based method outperforms strong baselines.

Deep neural network is very vulnerable to adversarial examples, which add subtle perturbations on the original image that are difficult for human to perceive, but can make network produce wrong classification results. The current advanced adversarial attack methods can achieve satisfactory results under the white-box setting, but when attacking the black-box model, especially for the defense models, they show poor transferability. It can mainly improve the transferability of adversarial attacks under black-box settings from two perspectives of gradient optimization and image transformation. We propose a new image transformation method, which is different from treating each pixel equally in previous works. We consider using the size of gradient value to reflect the importance of pixels, assigning different scaling factors to each gradient unit, and conducting heuristic random transformation on the images input in each iteration to achieve data enhancement, obtain more stable update direction and escape from local optimal values. Extensive experiments on ImageNet Dataset show that the proposed method has better performance than the existing methods. In addition, our method can also be combined with other attack methods to further improve the transferability of adversarial attacks. Besides, our approach also has excellent performance on the defense models.

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users’ intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user’s intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores.