Current recommendation methods typically use a unified framework to offer personalized recommendations for different scenarios provided by commercial platforms. However, they often employ shared bottom representations, which partially hinders the model's capacity to capture scenario uniqueness. Ideally, users and items should exhibit specific characteristics in different scenarios, prompting the need to learn scenario-specific representations to differentiate scenarios. Yet, variations in user and item interactions across scenarios lead to data sparsity issues, impeding the acquisition of scenario-specific representations. To learn robust scenario-specific representations, we introduce a Global-Distribution Aware Scenario-Specific Variational Representation Learning Framework (GSVR) that can be directly applied to existing multi-scenario methods. Specifically, considering the uncertainty stemming from limited samples, our approach employs a probabilistic model to generate scenario-specific distributions for each user and item in each scenario, estimated through variational inference (VI). Additionally, we introduce the global knowledge-aware multinomial distributions as prior knowledge to regulate the learning of the posterior user and item distributions, ensuring similarities among distributions for users with akin interests and items with similar side information. This mitigates the risk of users or items with fewer records being overwhelmed in sparse scenarios. Extensive experimental results affirm the efficacy of GSVR in learning more robust representations.

Traditional recommendation methods model a user's interest in a target item by correlating its embedding with the embeddings of items from the user's interaction history, thereby capturing implicit collaborative filtering signals. Consequently, traditional ID-based methods often encounter data sparsity problems stemming from the sparse nature of ID features. To mitigate this issue, recommendation models incorporate multimodal item information to enhance recommendation accuracy. However, existing multimodal recommendation methods typically rely on early fusion approaches, which focus primarily on combining text and image features, while neglecting the dynamic context provided by user behavior sequences. This oversight precludes the dynamic adaptation of multimodal interest representations to behavioral patterns, thereby hindering the model's ability to effectively capture user multimodal interests. Therefore, this paper proposes the Distribution-Guided Multimodal-Interest Auto-Encoder (DMAE), which achieves the cross fusion of user multimodal interest at the behavioral level. Specifically, DMAE comprises three key components: 1) Multimodal Interest Encoding Unit (MIEU), which encodes the similarity scores between the target item and historically clicked items as the corresponding representation vectors of user interest across different modalities. 2) Multimodal Interest Fusion Unit (MIFU), which dynamically adapts these interest representations through both intra- and inter-modal fusion, a process contextualized by the user's behavioral sequence to achieve a fine-grained and behavior-aware representation of interest. 3) Interest-Distribution Decoding Unit (IDDU), which employs a decoder to reconstruct the encoded user interest representations into true similarity distributions for each modality. The similarity distributions serve as a guide for model learning, aiming to retain as much multimodal information as possible. Ultimately, extensive experiments demonstrate the superiority of DMAE.

A number of V2X (Vehicle-to-Everything) messages are standardized by the European Telecommunication Standardization Institute (ETSI), such as CAM (Cooperative Awareness Message) and CPM (Collective Perception Message).Since road safety and traffic efficiency are on the basis of the assumption that correct and accurate V2V messages are shared, ensuring the trustworthiness of these V2X messages becomes an essential task in IoV (Internet of Vehicles) security.However, containing safety-related information makes V2X messages susceptible to malicious insider attacks from compromised vehicles after the PKI (Public Key Infrastructure) authentication step (Farran and Khoury, 2023), such as Ghost Vehicles (GV) (Gyawali and Qian, 2019), passively or actively reaching a 'ghost' state in terms of communication, position, etc.By integrating CPS (Collective Perception Service) in the Veins simulator, our work aims to propose a trust assessment model in IoV against several types of CAM-and CPM-based GV to increase security.The simulation results provide a preliminary analysis of the feasibility of the proposed model and show the effectiveness in terms of assessing V2X messages' trustworthiness.