The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essential form of passive data management. Multimodal sequential recommendation, which leverages diverse item information such as text and images, has shown great promise in enriching item representations and deepening the understanding of user interests. However, most existing models rely on heuristic fusion strategies that fail to capture the dynamic and context-sensitive nature of user-modal interactions. In real-world scenarios, user preferences for modalities vary not only across individuals but also within the same user across different items or categories. Moreover, the synergistic effects between modalities-where combined signals trigger user interest in ways isolated modalities cannot-remain largely underexplored. To this end, we propose CAMMSR, a Category-guided Attentive Mixture of Experts model for Multimodal Sequential Recommendation. At its core, CAMMSR introduces a category-guided attentive mixture of experts (CAMoE) module, which learns specialized item representations from multiple perspectives and explicitly models inter-modal synergies. This component dynamically allocates modality weights guided by an auxiliary category prediction task, enabling adaptive fusion of multimodal signals. Additionally, we design a modality swap contrastive learning task to enhance cross-modal representation alignment through sequence-level augmentation. Extensive experiments on four public datasets demonstrate that CAMMSR consistently outperforms state-of-the-art baselines, validating its effectiveness in achieving adaptive, synergistic, and user-centric multimodal sequential recommendation.

The ''pre-training, prompt-tuning'' has emerged as a pivotal paradigm in advancing the performance of graph representation learning models across a wide range of downstream tasks. This paradigm leverages the power of pre-trained models and task-specific prompts to bridge the gap between general graph representations and task-specific requirements. Early graph prompt tuning approaches relied on task-specific designs for Graph Neural Networks (GNNs), limiting their adaptability across diverse pre-training strategies. In contrast, another promising line of research has investigated universal graph prompt tuning, which operates directly in the input graph's feature space and builds a theoretical foundation that universal graph prompt tuning can theoretically achieve an equivalent effect of any prompting function, eliminating dependence on specific pre-training strategies. Recent works propose selective node-based graph prompt tuning to pursue more ideal prompts. However, we argue that selective node-based graph prompt tuning inevitably compromises the theoretical foundation of universal graph prompt tuning. In this paper, we strengthen the theoretical foundation of universal graph prompt tuning by introducing stricter constraints, demonstrating that adding prompts to all nodes is a necessary condition for achieving the universality of graph prompts. To this end, we propose a novel model and paradigm, Learning and Editing Universal GrAph Prompt Tuning (LEAP), which preserves the theoretical foundation of universal graph prompt tuning while pursuing more ideal prompts. Specifically, we first build the basic universal graph prompts to preserve the theoretical foundation and then employ actor-critic reinforcement learning to select nodes and edit prompts. Extensive experiments on graph- and node-level tasks across various pre-training strategies in both full-shot and few-shot scenarios show that LEAP consistently outperforms fine-tuning and other prompt-based approaches.

Although existing multimodal recommendation models have shown promising performance, their effectiveness continues to be limited by the pervasive data sparsity problem. This problem arises because users typically interact with only a small subset of available items, leading existing models to arbitrarily treat unobserved items as negative samples. To this end, we propose VI-MMRec, a model-agnostic and training cost-free framework that enriches sparse user-item interactions via similarity-aware virtual user-item interactions. These virtual interactions are constructed based on modality-specific feature similarities of user-interacted items. Specifically, VI-MMRec introduces two different strategies: (1) Overlay, which independently aggregates modality-specific similarities to preserve modality-specific user preferences, and (2) Synergistic, which holistically fuses cross-modal similarities to capture complementary user preferences. To ensure high-quality augmentation, we design a statistically informed weight allocation mechanism that adaptively assigns weights to virtual user-item interactions based on dataset-specific modality relevance. As a plug-and-play framework, VI-MMRec seamlessly integrates with existing models to enhance their performance without modifying their core architecture. Its flexibility allows it to be easily incorporated into various existing models, maximizing performance with minimal implementation effort. Moreover, VI-MMRec introduces no additional overhead during training, making it significantly advantageous for practical deployment. Comprehensive experiments conducted on six real-world datasets using seven state-of-the-art multimodal recommendation models validate the effectiveness of our VI-MMRec.

Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significant attention. However, existing multimodal recommendations overlook the critical role of user representation initialization. Unlike items, which are naturally associated with rich modality information, users lack such inherent information. Consequently, item representations initialized based on meaningful modality information and user representations initialized randomly exhibit a significant semantic gap.

Causality has been integrated with machine learning in uncovering and understanding the causal relationship between variables and observed outcomes. However, the centralized training setting of causal machine learning is not adaptable to most practical scenarios, where datasets are distributed, stored, and unsharable due to privacy concerns. Federated learning (FL), a distributed learning framework that allows collaborative training across multiple devices without raw data sharing, emerges as a potential solution to this problem. By integrating FL into causal problems, the discovery and inference of causal relationships across dispersed datasets can be achieved. On the other hand, causality can also enhance FL models in various dimensions, including model interpretability and explainability, generalizability, adversarial robustness, and fairness and bias mitigation. In this article, we provide a comprehensive review of the above two directions and summarize the interplays between causality and FL (short for Causal-FL ) by organizing our discussion around two key questions: (1) how FL enable decentralized causal analysis; and (2) how causality tackles FL challenges. The potential applications of these methods are also introduced, including healthcare, recommendation, economics, social equity, and so on. Moreover, we discuss promising future directions and future challenges to be explored.

Beyond traditional user recommendation, group recommendation is a new and popular task that provides recommendations for a group of users. Previous works aggregate member preferences in the group to infer group preference, but this often leads to a coarse-grained inference for group preferences limited by users’ individual preferences. To this end, we exploit that user preferences can be inferred and refined by exploring the group preferences that they participated in. These refined preferences offer additional information beyond the original individual preferences, enabling more fine-grained and satisfactory group preference inference. In this work, we propose a novel Dual-Granularity Graph Variational Auto-Encoder framework (DGGVAE) for group recommendation, which jointly reveals group preferences from both coarse granularity and fine granularity to comprehensively learn group preferences. Specifically, we design a Group Preference Extractor module that extracts group preferences from these two granularities: coarse granularity, which is revealed through original member preferences, and fine granularity, which is revealed through refined member preferences. To extract the correlation between groups, a Group Representation Enhancement module is proposed, which enhances group representations by information from the most similar groups. However, the coarse- and fine-grained group preferences contain uncertainty due to the gap between the original and refined member preferences. To better incorporate dual-granularity group preferences, we design granularity-specific graph variational encoders that learn Gaussian variables on the semantic information for each group. Moreover, with the conditional independence assumption, the granularity-specific Gaussian node embeddings are fused according to the generalized product-of-experts (gPoE), where the semantic information in each granularity is weighted based on the estimated uncertainty level. Extensive experiments show the superiority of DGGVAE over various state-of-the-art methods in training efficiency and accuracy on both group and user recommendation tasks.

Graph Collaborative Filtering (GCF) has emerged as a dominant paradigm in modern recommendation systems, excelling at modeling complex user-item interactions and capturing high-order collaborative signals. Most existing GCF models predominantly rely on simplified graph architectures like LightGCN, which strategically remove feature transformation and activation functions from vanilla graph convolution networks. Through systematic analysis, we reveal that feature transformation in message propagation can enhance model representation, though at the cost of increased training difficulty. To this end, we propose FourierKAN-GCF, a novel framework that adopts Fourier Kolmogorov-Arnold Networks as efficient transformation modules within graph propagation layers. This design enhances model representation while decreasing training difficulty. Our FourierKAN-GCF can achieve higher recommendation performance than most widely used GCF backbone models and can be integrated into existing advanced self-supervised models as a backbone, replacing their original backbone to achieve enhanced performance. Extensive experiments on three public datasets demonstrate the superiority of FourierKAN-GCF.

The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and item properties. While incorporating multimodal information can explicitly represent these preferences and properties, existing works often use it only as side information, failing to fully leverage its potential. In this paper, we propose MDVT, a model-agnostic approach that constructs multimodal-driven virtual triplets to provide valuable supervision signals, effectively mitigating the data sparsity problem in multimodal recommendation systems. To ensure high-quality virtual triplets, we introduce three tailored warm-up threshold strategies: static, dynamic, and hybrid. The static warm-up threshold strategy exhaustively searches for the optimal number of warm-up epochs but is time-consuming and computationally intensive. The dynamic warm-up threshold strategy adjusts the warm-up period based on loss trends, improving efficiency but potentially missing optimal performance. The hybrid strategy combines both, using the dynamic strategy to find the approximate optimal number of warm-up epochs and then refining it with the static strategy in a narrow hyper-parameter space. Once the warm-up threshold is satisfied, the virtual triplets are used for joint model optimization by our enhanced pair-wise loss function without causing significant gradient skew. Extensive experiments on multiple real-world datasets demonstrate that integrating MDVT into advanced multimodal recommendation models effectively alleviates the data sparsity problem and improves recommendation performance, particularly in sparse data scenarios.

Recent works in multimodal recommendations, which leverage diverse modal information to address data sparsity and enhance recommendation accuracy, have garnered considerable interest. Two key processes in multimodal recommendations are modality fusion and representation learning. Previous approaches in modality fusion often employ simplistic attentive or pre-defined strategies at early or late stages, failing to effectively handle irrelevant information among modalities. In representation learning, prior research has constructed heterogeneous and homogeneous graph structures encapsulating user-item, user-user, and item-item relationships to better capture user interests and item profiles. Modality fusion and representation learning were considered as two independent processes in previous work. This paper reveals that these two processes are complementary and can support each other. Specifically, powerful representation learning enhances modality fusion, while effective fusion improves representation quality. Stemming from these two processes, we introduce a COmposite grapH convolutional nEtwork with dual-stage fuSION for the multimodal recommendation, named COHESION. Specifically, it introduces a dual-stage fusion strategy to reduce the impact of irrelevant information, refining all modalities using behavior modality in the early stage and fusing their representations at the late stage. It also proposes a composite graph convolutional network that utilizes user-item, user-user, and item-item graphs to extract heterogeneous and homogeneous latent relationships within users and items. Besides, it introduces a novel adaptive optimization to ensure balanced and reasonable representations across modalities. Extensive experiments on three public datasets demonstrate the significant superiority of COHESION over various competitive baselines.

Multimodal recommender systems utilize a variety of information types to model user preferences and item properties, aiding in the discovery of items that align with user interests. Rich multimodal information alleviates inherent challenges in recommendation systems, such as data sparsity and cold start problems. However, multimodal information further introduces challenges in terms of robustness and generalization capability. Regarding robustness, multimodal information magnifies the risks associated with information adjustment and inherent noise, posing severe challenges to the stability of recommendation models. For generalization capability, multimodal recommender systems are more complex and difficult to train, making it harder for models to handle data beyond the training set, posing significant challenges to model generalization capability. In this paper, we analyze the shortcomings of existing robustness and generalization capability enhancement strategies in the multimodal recommendation field. We propose a sharpness-aware minimization strategy focused on batch data (BSAM), which effectively enhances the robustness and generalization capability of multimodal recommender systems without requiring extensive hyper-parameter tuning. Furthermore, we introduce a mixed loss variant strategy (BSAM+), which accelerates convergence and achieves remarkable performance improvement. We provide rigorous theoretical proofs and conduct experiments with nine advanced models on five widely used datasets to validate the superiority of our strategies. Moreover, our strategies can be integrated with existing robust training and data augmentation strategies to achieve further improvement, providing a superior training paradigm for multimodal recommendations.

In today’s fast-paced digital landscape, the attention span of users consuming video content is alarmingly brief, often as short as 15 seconds for music or entertainment videos and 6 minutes for lecture videos. This presents a significant challenge for video producers and platform providers as they seek to engage users with longer content. One promising solution involves recommending specific fragments within longer videos that align with individual user profiles. In this article, we address this challenge by introducing a novel framework for video fragment recommendations, guided by three key insights. First, we implement a Self-Attention Block that captures the inter-fragment contextual effect, enhancing the relevance of recommendations. Second, we incorporate video-level preferences to ensure that the fragment recommendations are consistent with users’ overall interests. Third, we propose a Self-Attentive Herding Effect (SAHE) module to model the intra-fragment contextual effect, specifically the herding effect of time-sync comments within a fragment. To evaluate the effectiveness of our proposed method, we conduct extensive experiments comparing our model against the state-of-the-art approaches in terms of NDCG@K and Recall@K. Our results demonstrate that the model effectively leverages inter-fragment and intra-fragment contextual effects along with video-level preferences, outperforming existing methods. Additionally, we carry out empirical experiments to analyze the key components and parameters of the proposed model, providing further insights into its performance. 1

Encrypted traffic classification is essential for network security and management. However, the encrypted nature makes it challenging to extract representative features from raw traffic data. Existing end-to-end methods ignore byte correlations within packets and potential correlations among packets, hindering the learning of real traffic semantics and leading to suboptimal performance. This paper proposes MsETC, a multi-scale contrastive attention representation learning method for encrypted traffic classification. MsETC divides the raw packet byte sequence into multi-scale patches and then extracts dual views for contrastive learning from both the inter-patch and intra-patch perspectives. This allows the model to capture correlations among bytes within a packet as well as the potential interactions between packets. Extensive experiments on real-world datasets demonstrate that the proposed method achieves superior classification performance with lower complexity.

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.

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this article, we address this challenge by proposing Auto-weighted Robust Federated Learning ( ARFL ), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected loss with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. We present an objective that minimizes the weighted sum of empirical risk of clients with a regularization term, where the weights can be allocated by comparing the empirical risk of each client with the average empirical risk of the best \( p \) clients. This method can downweight the clients with significantly higher losses, thereby lowering their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from the benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct extensive experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST, and Shakespeare, considering different neural network models. The results show that our solution is robust against different scenarios, including label shuffling, label flipping, and noisy features, and outperforms the state-of-the-art methods in most scenarios.

Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.