Sequential recommendation (SR) aims to predict users' next action based on their historical behavior, and is widely adopted by a number of platforms. The performance of SR models relies on rich interaction data. However, in real-world scenarios, many users only have a few historical interactions, leading to the problem of data sparsity. Data sparsity not only leads to model overfitting on sparse sequences, but also hinders the model’s ability to capture the underlying hierarchy of user intents. This results in misinterpreting the user's true intents and recommending irrelevant items. Existing data augmentation methods attempt to mitigate overfitting by generating relevant and varied data. However, they overlook the problem of reconstructing the user's intent hierarchy, which is lost in sparse data. Consequently, the augmented data often fails to align with the user's true intents, potentially leading to misguided recommendations. To address this, we propose the Adaptive Diffusion Augmentation for Recommendation (ADARec) framework. Critically, instead of using a diffusion model as a black-box generator, we use its entire step-wise denoising trajectory to reconstruct a user's intent hierarchy from a single sparse sequence. To ensure both efficiency and effectiveness, our framework adaptively determines the required augmentation depth for each sequence and employs a specialized mixture-of-experts architecture to decouple coarse- and fine-grained intents. Experiments show ADARec outperforms state-of-the-art methods on standard benchmarks and on sparse sequences, demonstrating its ability to reconstruct hierarchical intent representations from sparse data.

Real-world text classification datasets frequently exhibit long-tail distributions, where numerous classes have sparse data, significantly degrading model performance on these underrepresented categories. While Large Language Models (LLMs) offer promise for data augmentation, existing methods often produce semantically limited samples, neglect "implicit long-tails" (sparse sub-patterns within classes), and lack cost-effective optimization. To address these challenges, we propose \textbf{DEALT (LLM-driven Diversity-Enhanced Data Augmentation for Long-Tail Text Classification)}, a novel cognitive-inspired framework emulating the human learning process of "recognize, explore, generate, and optimize." DEALT systematically enhances augmented data diversity by first detecting both explicit and implicit long-tails. It then employs an LLM for diversity-aware planning of augmentation strategies, followed by conditional generation. A low-overhead quality and diversity validator filters the synthetic data, and an adaptive incremental sampler refines future augmentation efforts based on proxy model feedback, ensuring efficient and budget-aware optimization. Extensive experiments on multiple public text classification datasets demonstrate DEALT's superiority over state-of-the-art methods in improving tail-class performance and overall model robustness by generating more diverse and high-fidelity augmented data.

Traffic flow prediction is crucial for efficient urban planning, traffic management, and user navigation. Modern deep learning models have achieved great success in capturing the complex spatio-temporal dependencies in traffic networks. However, due to frequently changing traffic patterns, the performance of deployed models degrades over time, necessitating periodic updates. Full-scale model retraining is computationally expensive, creating a critical conflict between maintaining prediction accuracy and minimizing update overhead. To address this, incremental update or continual learning methods have emerged, but existing approaches are often tightly coupled with specific model architectures and rely on unclear criteria for data selection, thereby causing redundant data selection and lacking interpretability. To overcome these limitations, we propose a lightweight continual learning method based on B-splines. This method identifies the most valuable data for model updates by analyzing the intrinsic geometric and statistical properties of the traffic data itself. Specifically, we fit a B-spline curve to create a smooth representation of the core traffic pattern and then compute the regression leverage score for each data point to quantify its structural importance. This strategy decouples the data evaluation process from the internal mechanisms of the prediction model. Since the selected data points directly reflect key features of the traffic pattern-such as peaks, inflection points, and anomalies-our method is inherently interpretable, allowing users to understand why certain data points are chosen. Extensive experiments on multiple real-world datasets demonstrate that our method maintains a high level of prediction accuracy while significantly reducing the computational cost of model updates, offering an efficient and transparent solution for the maintenance of dynamic traffic systems.

Spatial crowdsourcing has become popular in recent years, but traditional tasks focus on one-to-one services with single skills like food delivery and ride-hailing. As societal needs grow more complex, there is a need for tasks requiring teams with multiple skills. Current team formation methods using workers from a single platform limit skill diversity, leading to potential task delays, lower quality, and revenue losses. Although cross-platform cooperation offers a potential solution to skill diversity limitations, it faces two challenges: (1) Data protection regulations mandate that platform's raw data must remain localized; (2) cross-platform cooperation incurs additional cooperation costs. To address these challenges, we first define the Cross-platform Online Team Formation (COTF) problem. We then propose a COTF framework and Random Cooperation Strategy to solve COTF problem. To enhance the effectiveness of cooperation, we further propose Precision Query Range Optimization Strategy (PQROS) for worker selection through adaptive range queries, and Dynamic Query Optimization (DQO) for cost-effective scheduling via predictive revenue modeling. Extensive experiments on real and synthetic datasets validate the effectiveness of our proposed methods.

Lately, the academic community has been showing growing interest in multi-domain fake news detection, and in particular, incorporating multimodal information into this field has emerged as a highly promising research direction. However, existing methods often struggle with: (1) Insufficient intrinsic domain adaptation during representation Learning; (2) Amplified negative transfer from entangled domain style and content representations; and (3) Neglecting domain-varying modality uncertainty. To address these issues, we propose Domain-Aware Prompt Tuning (DAPT), an innovative framework for multimodal multi-domain fake news detection. DAPT leverages Multimodal Prompt Tuning for parameter-efficient domain adaptation of pretrain models. An Adaptive Domain Debias Module disentangles domain features from veracity signals guided by content to mitigate negative transfer. Furthermore, inspired by the Variational Information Bottleneck, an Uncertainty-Aware Multimodal Fusion mechanism adaptively aggregates modalities based on domain-specific reliability. Extensive experiments demonstrate that DAPT significantly outperforms state-of-the-art baselines on benchmark datasets.

In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to comprehensively model users' multi-modal preferences. Therefore, it is necessary to introduce multi-modal CTR prediction. Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities.

The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences across different behaviors, as user attention to different item modalities varies depending on the behavior; (2) the difficulty of effectively mitigating implicit noise in user behavior, such as unintended actions like accidental clicks; (3) the inability to handle modality noise in multi-modal representations, which further impacts the accurate modeling of user preferences. To tackle these issues, we propose a novel Multi-Modal Multi-Behavior Sequential Recommendation model (M3BSR). This model first removes noise in multi-modal representations using a Conditional Diffusion Modality Denoising Layer. Subsequently, it utilizes deep behavioral information to guide the denoising of shallow behavioral data, thereby alleviating the impact of noise in implicit feedback through Conditional Diffusion Behavior Denoising. Finally, by introducing a Multi-Expert Interest Extraction Layer, M3BSR explicitly models the common and specific interests across behaviors and modalities to enhance recommendation performance. Experimental results indicate that M3BSR significantly outperforms existing state-of-the-art methods on benchmark datasets.

With the development of AI, big data, and mobile communication, intelligent transportation has become popular in recent years. Path planning is a typical topic of intelligent transportation, attracting significant attention from researchers. However, existing studies only focus on the path planning of a single platform, which may lead to unexpected traffic congestion. This is because multiple platforms can provide route planning services, the optimal planning calculated by one single platform may be not good in practice, since multiple platforms may lead the users to the same roads, which causes unexpected traffic congestion. Although in the view of each platform, the planning is optimal. Fortunately, with the rise of data sharing and cross-platform cooperation, the data silos between different platforms are gradually being broken. Based on this, we proposeCooperativeGlobalPathPlanning(CGPP) framework to overcome the above shortcoming. CGPP allows the path planning request target platform to send some queries to cooperative platforms to optimize its path planning results. Such queries should be “easy” enough to answer, and the query frequency should be small. Based on the above principle, we design a query decision model based on multi-agent reinforcement learning in CGPP framework to decide the query range and query frequency. We design action and reward specifically for the CGPP problem. Furthermore, we propose mechanisms to enhance query precision and reduce query overhead. Specifically, the Self-adjusting Query Area(SQA) concept allows refining query parameters, while the Query Reuse Optimization(QRO) algorithm aims to minimize the number of queries. To solve potential overestimation problems in queries, we propose a Distance-based Outer Query (DB-oq) and Distance-Based Vehicle Count Estimation (DB-VCE) Model. To address the issue that the time interval computed by the QRO algorithm might not fully adapt to dynamic traffic environments, we propose the Temporal Sequence Historical Integration for Time Interval Prediction(TSHI-TIP) algorithm. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.

With the development of AI, big data, and mobile communication, intelligent transportation has become popular in recent years. Path planning is a typical topic of intelligent transportation, attracting significant attention from researchers. However, existing studies only focus on the path planning of a single platform, which may lead to unexpected traffic congestion. This is because multiple platforms can provide route planning services, the optimal planning calculated by one single platform may be not good in practice, since multiple platforms may lead the users to the same roads, which causes unexpected traffic congestion. Although in the view of each platform, the planning is optimal. Fortunately, with the rise of data sharing and cross-platform cooperation, the data silos between different platforms are gradually being broken. Based on this, we propose Cooperative Global Path Planning (CG PP) framework to over-come the above shortcoming. CGPP allows the path planning request target platform to send some queries to cooperative platforms to optimize its path planning results. Such queries should be “easy” enough to answer, and the query frequency should be small. Based on the above principle, we design a query decision model based on multi-agent reinforcement learning in CGPP framework to decide the query range and query frequency. We design action and reward specifically for the CGPP problem. Furthermore, we propose the Self-adjusting Query Area algorithm to enhance the precision of query results and the Query Reuse Optimization algorithm to further minimize the number of queries. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.