Bundle recommendation aims to provide users with a one-stop service by offering a collection of related items. However, these systems face a significant challenge, where a small portion of bundles accumulate most interactions while the long-tail bundles receive few interactions.This imbalance leads to poor performance for long-tail bundles despite their potential to satisfy diverse user needs. Existing long-tail item recommendation methods fail to effectively address this problem, as long-tail bundle recommendation requires not only capturing the user-bundle interactions but also the item compositions in different bundles. Therefore, in this paper, we propose Composition-Aware Long-tail Bundle Recommendation (CALBRec), which leverages the inherent composition patterns shared across different bundles as valuable signals for further representation augmentation and recommendation enhancement. Specifically, to solve the complexity of modeling shared composition patterns due to the exponential explosion caused by the growing number of items and bundle sizes, we first introduce a composition-aware tail adapter to capture the shared composition patterns and then adaptively integrate them into individual bundle representations. Moreover, to mitigate the impact of noise in user-bundle interaction data, we propose to map the bundle representations into a set of learnable prototypes, and we further propose a prototype learning module to combine the composition patterns with interaction signals for tail bundles. Extensive experiments on three public datasets demonstrate that our method can improve the performance on bundle recommendation significantly, especially on the long-tail bundles.

Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Existing denoising studies involve incorporating auxiliary information or learning strategies from interaction data. However, they struggle with the inherent limitations of external knowledge and interaction data, as well as the non-universality of certain predefined assumptions, hindering accurate noise identification. Recently, large language models (LLMs) have gained attention for their extensive world knowledge and reasoning abilities, yet their potential in enhancing denoising in recommendations remains underexplored. In this paper, we introduce LLaRD, a framework leveraging LLMs to improve denoising in recommender systems, thereby boosting overall recommendation performance. Specifically, LLaRD generates denoising-related knowledge by first enriching semantic insights from observational data via LLMs and inferring user-item preference knowledge. It then employs a novel Chain-of-Thought (CoT) technique over user-item interaction graphs to reveal relation knowledge for denoising. Finally, it applies the Information Bottleneck (IB) principle to align LLM-generated denoising knowledge with recommendation targets, filtering out noise and irrelevant LLM knowledge. Empirical results demonstrate LLaRD's effectiveness in enhancing denoising and recommendation accuracy.

Video Salient Object Detection (VSOD) faces significant challenges due to complex background disturbances in video sequences. Although notable progress has been made in this field, further efforts are needed to better address background interference. To address these challenges, our scene-aware background decoupling network (SBDNet) equipped with a scene-aware background decoupling strategy (SBDS) as well as collaborative fusion decoders (CFD). The SBDS includes a Dynamic Background Disentanglement Module (DBD) designed to effectively eliminate background distractions in real-world scenes. The DBD achieves this by utilizing a background mask to refine foreground elements and extracting semantic-enhanced context weights. The CFD enhances the fusion between CNN and Transformer decoders, ensuring high accuracy in saliency detection for video sequences. Extensive results demonstrate that our SBDNet significantly outperforms 14 state-of-the-art methods on four widely used benchmark datasets.

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To address this issue, a prevailing idea is to learn stable features, on the assumption that they are substructures causally determining the label and that their relationship with the label is stable to the distributional uncertainty. In contrast, the complementary parts termed environmental features, fail to determine the label solely and hold varying relationships with the label, thus ascribed to the possible reason for the distribution shift. Existing generalization efforts mainly encourage the model’s insensitivity to environmental features. While the sensitivity to stable features is promising to distinguish the crucial clues from the distributional uncertainty but largely unexplored. A paradigm of simultaneously exploring the sensitivity to stable features and insensitivity to environmental features is until-now lacking to achieve the generalizable graph classification, to the best of our knowledge. In this work, we conjecture that generalizable models should be sensitive to stable features and insensitive to environmental features. To this end, we propose a simple yet effective augmentation strategy for graph classification: Equivariant and Invariant Cross-Data Augmentation (EI-CDA). By employing equivariance, given a pair of input graphs, we first estimate their stable and environmental features via masks. Then, we linearly mix the estimated stable features of two graphs and encourage the model predictions faithfully reflect their mixed semantics. Meanwhile, by using invariance, we swap the estimated environmental features of two graphs and keep the predictions invariant. This simple yet effective strategy endows the models with both sensitivity to stable features and insensitivity to environmental features. Extensive experiments show that EI-CDA significantly improves performance and outperforms leading baselines. Our codes are available at: https://github.com/yongduosui/EI-GNN .

In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite considerable advancements in model compression and architecture search, prevalent approaches face notable constraints. These include substantial additional computational costs from pre-training/re-training in model compression and an extensive search space in architecture design. Additionally, managing complexity and adhering to memory constraints is problematic, especially in scenarios with strict time or space limitations. Addressing these issues, this paper introduces a novel learning paradigm, Dynamic Sparse Learning (DSL), tailored for recommendation models. DSL innovatively trains a lightweight sparse model from scratch, periodically evaluating and dynamically adjusting each weight's significance and the model's sparsity distribution during the training. This approach ensures a consistent and minimal parameter budget throughout the full learning lifecycle, paving the way for "end-to-end" efficiency from training to inference. Our extensive experimental results underline DSL's effectiveness, significantly reducing training and inference costs while delivering comparable recommendation performance. We give an code link of our work: https://github.com/shuyao-wang/DSL.

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate inherent limitations in collaborative filtering (CF), such as data sparsity or cold-start issues. However, most existing knowledge-aware recommendation models indiscriminately aggregate all information in KG, without considering information specifically relevant to the recommendation task. Such indiscriminate aggregation could introduce additional noisy knowledge into representation learning, which can distort the understanding of users' genuine preferences, thereby sacrificing the recommendation quality. In this paper, we introduce the principle of invariance to the knowledge-aware recommendation, culminating in our Knowledge Graph Invariant Learning (KGIL) framework. It aims to discern and harness the task-relevant knowledge connections within KG to enhance the recommendation models. Specifically, we employ multiple environment generators to simulate diverse noisy KG-environments. Then we devise a novel attention learning mechanism for KG and user-item interaction graph, aiming to learn environment-invariant subgraphs. Leveraging an adversarial optimization strategy, we enhance the diversity of the environments, meanwhile, promote invariant representation learning across environments. We conduct extensive experiments on three datasets and compare KGIL with state-of-the-art methods. The experimental results further demonstrate the superiority of our approach.

In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend,” which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this article, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory—combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method’s effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus .

Adversarial training (AT) commonly serves as an advanced regularization to establish enhanced robust models. However, it usually scarifies performance on clean inputs, especially in complicated object detection and semantic segmentation tasks. However, how to fully unleash the power of adversarial training regularization to improve the trade-off between standard performance and adversarial robustness of detection and segmentation models, has not been explored. In this paper, we present the Vertical and Horizontal Adversarial Training (VHAT) regularization on both input and intermediate features, which consists of two major components: i) Vertical Adversarial Training (VAT) by utilizing adversarial features with a wide range of attack strengths; ii) Horizontal Adversarial Training (HAT) by injecting layer-wise adversarial feature perturbations together with adversarial samples. Extensive experiment results demonstrate that VHAT achieves the standard performance and adversarial robustness double-win for Faster-RCNN on PASCAL VOC and DeepLabv3+ on PASCAL VOC and Cityscapes datasets, respectively. Comprehensive ablation studies and visualizations are provided to reveal the insights and working mechanisms.

In this paper we propose a new snow water equivalent (SWE) estimation method using GNSS-R method. The forward model is established to describe the relationship between antenna height (snow depth), snow density and multipath error by using combination of pseudorange and carrier-phase of GNSS dual-frequency signals. As the function of antenna height and snow density, the forward model is used to construct the fitness function based on least squares principle. Then, the problem of inversion of antenna height and snow density is transformed into a conventional multi-variable function optimization problem. The genetic algorithm is used to find the minimum of the fitness function to obtain the optimal snow depth and snow density estimates. The Galileo satellite navigation system data of an experimental campaign conducted in Harbin, China was used to test the proposed method. The preliminary results show that the proposed method can achieve SWE estimation accuracy of about 4cm.

Snow is an important part of freshwater resources. Accurately measuring the snow depth is of great significance for studying the hydrological cycle and preventing flood hazards. In addition to the traditional ground -based direct measurement, snow depth can also be estimated by the spaceborne or airborne remote sensing. Compared with the traditional method, the latter has advantages in resource optimization and data processing. GNSS Reflectometry (GNSS-R) as an emerging technology can be used to estimate snow depth. In this paper, we present a new method to estimate snow depth. The method combines the carrier phase observations of GPS dual-frequency (L1 and L2) obtained by the dual-receiver system. This phase combination is geometry free and is not affected by ionospheric delays. A theoretical model is established to describe the relationship between the snow depth and the spectral peak frequency of the combined phase. In the actual snow depth estimation process, the carrier phase observation data recorded by GNSS receivers are processed to obtain the spectral peak frequency which is then used to calculate the snow depth based on the developed model.

Image quality in mobile displays is considerably influenced by ambient light. In the daylight condition, images on mobile displays are darkly perceived by the human visual system due to the limited dynamic range of display, which causes loss of luminance and details. In this paper, we propose readability enhancement of displayed images under ambient light by enhancing both luminance and details. We design a weighted optimization framework, which contains the data term for luminance enhancement and the gradient term for detail enhancement. In the data term, we use an ambient light nonlinear intensity-transfer function considering display properties, ambient light, and image contents. In the gradient term, we employ a threshold versus intensity adaptation function based on the degree of ambient light adaptation to compensate for gradient distortions. We solve the optimization framework using a numerical solver and achieve image enhancement in displayed images. Experimental results demonstrate that the proposed method significantly improves readability of mobile displays under ambient light by enhancing luminance and details of images.

Image quality of mobile displays are significantly influenced by ambient light. In the daylight condition, displayed images on mobile displays are darkly perceived by human visual system (HVS), which suffer from significant detail loss. However, only luminance enhancement seriously affects image details especially for bright regions. To overcome this problem, we propose a quadratic optimization framework which includes data term for luminance enhancement and gradient term for detail enhancement. In the data term, we provide an ambient light nonlinear intensity-transfer function for adaptive luminance enhancement depending on display properties, ambient light, and image contents. In the gradient term, Weber's law is employed for detail enhancement. Finally, we achieve both luminance and detail enhancement by solving the optimization framework. Experimental results demonstrate that the proposed method remarkably enhances the visibility of displayed images under strong ambient light.