As multimedia information proliferates, multimodal recommendation systems have garnered significant attention. These systems leverage multimodal information to alleviate the data sparsity issue inherent in recommendation systems, thereby enhancing the accuracy of recommendations. Due to the natural semantic disparities among multimodal features, recent research has primarily focused on cross-modal alignment using self-supervised learning to bridge these gaps. However, aligning different modal features might result in the loss of valuable interaction information, distancing them from ID embeddings. It is crucial to recognize that the primary goal of multimodal recommendation is to predict user preferences, not merely to understand multimodal content. To this end, we propose a new Multi-level sElf-supervised learNing for mulTimOdal Recommendation (MENTOR) method, which effectively reduces the gap among modalities while retaining interaction information. Specifically, MENTOR begins by extracting representations from each modality using both heterogeneous user-item and homogeneous item-item graphs. It then employs a multilevel cross-modal alignment task, guided by ID embeddings, to align modalities across multiple levels while retaining historical interaction information. To balance effectiveness and efficiency, we further propose an optional general feature enhancement task that bolsters the general features from both structure and feature perspectives, thus enhancing the robustness of our model.

Edge Intelligence (EI) has been instrumental in delivering real-time, localized services by leveraging the computational capabilities of edge networks. The integration of Large Language Models (LLMs) empowers EI to evolve into the next stage: Edge General Intelligence (EGI), enabling more adaptive and versatile applications that require advanced understanding and reasoning capabilities. However, systematic exploration in this area remains insufficient. This survey delineates the distinctions between EGI and traditional EI, categorizing LLM-empowered EGI into three conceptual systems: centralized, hybrid, and decentralized. For each system, we detail the framework designs and review existing implementations. Furthermore, we evaluate the performance and throughput of various Small Language Models (SLMs) that are more suitable for deployment on edge devices. This survey provides researchers with a comprehensive vision of EGI, offering insights into its vast potential and establishing a foundation for future advancements in this rapidly evolving field.

The exponential growth of online information has made it increasingly difficult for users to identify valuable and relevant content. Recommender systems have emerged as a critical solution to this challenge by tailoring content to individual preferences. With the proliferation of diverse multimedia services, human interaction with the digital world has become inherently multimodal. Consequently, recommender systems capable of comprehending and interpreting multimodal information can more effectively align with individual preferences. With its recent surge in research attention, the field of multimodal recommender systems (MRS) still lacks a comprehensive technical survey. Existing surveys suffer from two critical limitations: 1) Insufficient technical depth: Prior works predominantly focus on categorizing and discussing general structure, neglecting rigorous technical analysis of methodologies and architectures. 2) Absence of cutting-edge works: Due to the rapid evolution of AI technologies, current surveys fail to discuss the latest works that adopt the most advanced techniques. To bridge these gaps, this survey conducts a systematic and technical review of advanced MRS works from MRS's inception to the present. We organize existing works into coherent taxonomies based on their structure and provide in-depth analyses of methodological innovations at each component, including Feature Extraction, Encoder, Multimodal Fusion, and Loss Function. Moreover, we further discuss potential future directions for developing and enhancing MRS. This survey serves as technical guidance for researchers and practitioners, offering insights into the developments, techniques, and future directions of MRS. Notably, we maintain a public repository: https://github.com/Jinfeng-Xu/Awesome-Multimodal-Recommender-Systems.

Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can usually be categorized into two strategies to infer group preferences: 1) determining group preferences by aggregating members' personalized preferences, and 2) inferring group consensus by capturing group members' coherent decisions after common compromises. However, the former would suffer from the lack of group-level considerations, and the latter overlooks the fine-grained preferences of individual users. To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. Specifically, AlignGroup explores group consensus through a well-designed hypergraph neural network that efficiently learns intra- and inter-group relationships. Moreover, AlignGroup innovatively utilizes a self-supervised alignment task to capture fine-grained group decision-making by aligning the group consensus with members' common preferences. Extensive experiments on two real-world datasets validate that our AlignGroup outperforms the state-of-the-art on both the group recommendation task and the user recommendation task, as well as outperforms the efficiency of most baselines.

The deployment of learning-based models to detect malicious activities in network traffic flows is significantly challenged by concept drift. With evolving attack technology and dynamic attack behaviors, the underlying data distribution of recently arrived traffic flows deviates from historical empirical distributions over time. Existing approaches depend on a significant amount of labeled drifting samples to facilitate the deep model to handle concept drift, which faces labor-intensive manual labeling and the risk of label noise. In this paper, we propose ReCDA, a Concept Drift Adaptation method with Representation enhancement, which consists of a self-supervised representation enhancement stage and a weakly-supervised classifier tuning stage. Specifically, in the initial stage, ReCDA introduces drift-aware perturbation and representation alignment to facilitate the model in acquiring robust representations from drift-aware and drift-invariant perspectives. Moreover, in the subsequent stage, a meticulously crafted instructive sampling strategy and a robust representation constraint encourage the model to learn discriminative knowledge about benign and malicious activities during fine-tuning, thereby enhancing performance further. We conduct comprehensive evaluations on several benchmark datasets under varying degrees of concept drift. The experiment results demonstrate the superior adaptability and robustness of the proposed method.