The proliferation of fake news on short video platforms has recently attracted widespread attention, prompting a growing interest in fake news video detection. While existing studies show initial success, they may be constrained by the following limitations: (1) Current detection approaches lack logical understanding and integration capabilities, thereby leading to models missing implicit essential news information. (2) They simultaneously exploit news content for detection but fail to effectively distinguish between informative and non-informative elements, causing the attention bias within the detection model. To address these limitations, we propose a Denoising-Enhanced Multimodal Detection Network (D2et) to mine implicit essential news information and filter out redundant information. Firstly, to improve comprehension of essential content, we leverage the large language model to mine tags from two levels: (1) Global-Insight Tag, which captures the overall content of the short video; and (2) Local-Veracity Tag, which identifies essential details. We obtain the global and local level tags to jointly extract the explicit and structured content for clarifying essential information. Secondly, we design an information denoising mechanism to eliminate redundant noise. This mechanism refines multimodal features guided by the extracted tags, ensuring that the model focuses on relevant content and suppresses redundant information, thereby improving detection performance. Extensive experiments on two datasets demonstrate the effectiveness of our method, validating the contributions of multi-level tags and the information denoising mechanism.

Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details.We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain items’ content features exhibit the issues of information drift and information omission, reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behaviordriven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods.

Graph Neural Network (GNN)-based methods have recently emerged as effective approaches for multimedia recommendation. Typically, these methods employ message passing on the user-item interaction graph, and model user preferences by exploiting co-occurrence patterns. Despite their effectiveness, we argue that they insufficiently exploit the individual information, potentially limiting recommendation performance. To validate our argument, we first analyze existing methods from spectral graph theory. We identify that existing methods focus on capturing global structural features, but underutilize local structural features that convey individual information. Further detailed experiments reveal that such an underutilization leads to overly similar user preferences modeling. Furthermore, we propose a novel Principal Graph Learning (PGL) framework to address this issue. The idea is to enhance user preference modeling by effectively mining and utilizing principal local structural features. PGL first extracts the principal subgraph from the user-item interaction graph using two novel extraction operators: global-aware and local-aware subgraph extraction. It then employs message passing on the principal subgraph to comprehensively model user perference, with the aim of simultaneously capturing co-occurrence patterns and individual information. Compared to existing methods, PGL achieves an average performance improvement of 9%.

Graph Collaborative Filtering is a widely adopted approach for recommendation, which captures similar behavior features through Graph Neural Network (GNN). Recently, Contrastive Learning (CL) has been demonstrated as an effective method to enhance the performance of graph collaborative filtering. Typically, CL-based methods first perturb users’ history behavior data (e.g., drop clicked items), then construct a self-discriminating task for behavior representations under different random perturbations. However, for widely existing inactive users, random perturbation makes their sparse behavior information more incomplete, thereby harming the behavior feature extraction. To tackle the above issue, we design a novel directional perturbation-based CL method to improve the graph collaborative filtering performance. The idea is to perturb node representations through directionally enhancing behavior features. To do so, we propose a simple yet effective feedback mechanism, which fuses the representations of nodes based on behavior similarity. Then, to avoid irrelevant behavior preferences introduced by the feedback mechanism, we construct a behavior self-contrast task before and after feedback, to align the node representations between the final output and the first layer of GNN. Different from the widely adopted self-discriminating task, the behavior self-contrast task avoids complex message propagation on different perturbed graphs, which is more efficient than previous methods. Extensive experiments on three public datasets demonstrate that the proposed method has distinct advantages over other CL methods on recommendation accuracy.

Multimedia recommendation has received much attention in recent years. It models user preferences based on both behavior information and item multimodal information. Though current GCN-based methods achieve notable success, they suffer from two limitations: (1) Modality noise contamination to the item representations. Existing methods often mix modality features and behavior features in a single view (e.g., user-item view) for propagation, the noise in the modality features may be amplified and coupled with behavior features. In the end, it leads to poor feature discriminability; (2) Incomplete user preference modeling caused by equal treatment of modality features. Users often exhibit distinct modality preferences when purchasing different items. Equally fusing each modality feature ignores the relative importance among different modalities, leading to the suboptimal user preference modeling.