Modern applications provided personalized recommendations across diverse scenarios, including the homepage, local pages, and live streams on platforms like TikTok. These scenarios exhibit varying user behavior patterns, resulting in heterogeneous yet interrelated distributions. Existing Multi-Scenario Recommendation (MSR) methods usually use parameter-sharing networks for shared features and scenario-specific networks for unique features. However, these methods fail to handle different distribution across scenarios, resulting in representation entanglement and localization, which hinder effective knowledge transfer and compromise performance. In this paper, we propose a Distribution-aware Re-representations (DAR) method for MSR. Its core idea is to construct distribution-aware prototype spaces and learn disentangled re-representations around global prototypes. Specifically, DAR employs a Multi-gate Mixture of Experts (MMoE) to obtain scenario-shared representations, and uses independent networks to learn scenario-specific representations. These representations are then projected into scenario-shared and scenario-specific prototype spaces, producing scenario-shared re-representations (capturing global information) and scenario-specific re-representations (focusing on distributional differences). During this process, DAR utilizes Unbalanced Optimal Transport (UOT) to compute the transport relationships between representations and global prototypes, taking these as pseudo-labels for re-representation learning. Moreover, to prevent prototype entanglement, a matrix orthogonalization constraint ensures independence among global prototypes. The effectiveness of DAR is demonstrated through extensive offline experiments conducted on four datasets, as well as online A/B tests on a video platform.

Mobile edge computing (MEC) is important in location-based social networks (LBSNs). It puts services near users to cut delays. Edge service recommendation needs to mix context details with user privacy. Data sparsity makes this hard. Traditional methods have trouble with little data. They miss small context details or hurt privacy with central systems. This paper introduces FedCAFE, a federated learning system for edge service recommendation with context awareness. FedCAFE uses three main parts. It has a denoising autoencoder to get strong user and service features from small data. This tool learns patterns by fixing noisy information. It helps when user-service interactions are few. FedCAFE also uses a new adaptive fuzzy clustering method to group users and services by context matches. This part looks at things like time and place. It changes how it groups based on different situations. FedCAFE applies federated learning to keep privacy safe. It trains on user devices. It sends only model updates, not personal data. This stops private stuff like location from leaving the device. We tested FedCAFE on the WSDream dataset with real service information. FedCAFE beats other methods in these tests.

Online video recommendation systems often build binary labels based on play complete rate (i.e., the ratio of watch time to video duration), such as complete play and effective play, using them as implicit feedback for Click-Through Rate (CTR) prediction tasks to gauge user interest. Existing works tend to improve prediction accuracy by designing complex models, overlooking that a key cause of inaccurate predictions is the disorganization of instance representation space. To address this issue, we explore a novel approach using prototype learning to calibrate the instance representation space of deep recommendation models and propose a model-agnostic Contrastive Prototype Framework (CPF). Firstly, CPF partitions the instance space into different subspaces based on duration, then generates positive and negative prototype pairs for each subspace from pre-trained recommendation model. Subsequently, we map the instance representations to the prototype space and calibrate them by reducing the distance to the corresponding prototypes. Ultimately, the prediction is derived from the linear combination of the estimated values associated with each prototype. To prevent disorganization in the prototype space during training, we design contrastive and orthogonality losses to constrain the learning of prototypes. Additionally, we show that how CPF effectively addresses the duration bias from the perspective of causal intervention. Offline experiments on two datasets demonstrate that CPF improves recommendation accuracy over several baseline models in predicting five widely used implicit feedback labels. We have also deployed CPF on a short video platform, validating its effectiveness in real-world scenarios.

The rapid dissemination of multimodal fake news on social media platforms poses a significant challenge, as deceptive narratives often combine text and images to mislead users. To address this, we propose MEVE-FN, a novel fake news detection framework built upon a Mixture-of-Experts (MoE) architecture. Our model leverages modality-specific vision and language experts to extract specialized features. These features are then dynamically integrated using a Laplace Gating mechanism for adaptive fusion and a MEVE-Adapter module that enhances deep cross-modal interaction. Comprehensive experiments on three public benchmarks (Weibo, Weibo21, and Twitter) demonstrate that MEVE-FN consistently outperforms strong baseline models, achieving notable improvements in detection accuracy and robustness. Furthermore, ablation studies validate the effectiveness of our proposed components, confirming the critical contribution of both the Laplace gating and the MEVE-Adapter to the model’s superior performance.

Inductive knowledge graph completion (KGC) aims at predicting triples involving new entities or relations not present during training. Recently proposed methods have achieved good performance in predicting triples involving only unseen entities, which either utilize the enclosing subgraph reasoning or learn transferable structural patterns by sampling local subgraphs. However, existing methods predominantly focus on modeling entities based on neighboring relations within independent subgraphs, posing challenges in handling sparse knowledge graphs and leading to the loss of global semantic information. In this paper, we introduce MeJo, a meta-learning-based joint two-view framework. MeJo incorporates the ontology view to provide rich, transferable type information for entity representation. The two-view interaction connects each independent subgraph, enabling the model to learn global contextual information. The model is trained to capture transferable structure knowledge from the instance view and comprehensive semantic information from the ontology view, incorporating hierarchy-aware encoding for ontologies with hierarchical structures. Furthermore, our approach can extend to handling both unseen entities and unseen relations simultaneously during the test. Extensive experimental analysis reveals that MeJo excels beyond current state-of-the-art approaches in both effectiveness and generalizability across prevalent benchmark datasets.