Search Results (75)

Recommender systems are central to helping users navigate the rapidly expanding entertainment ecosystem, yet achieving strong personalization with limited feedback while maintaining interpretability remains difficult, particularly under cold-start conditions and heterogeneous item metadata. This work presents an end-to-end hybrid recommendation framework that unifies a Large Language Model (LLM) with Graph Attention Network (GAT)-based collaborative filtering to improve both ranking accuracy and explanation quality across movies, books, and music. LLM-based agents first transform raw metadata such as titles, genres, descriptions, and auxiliary attributes into semantically grounded user and item profiles, which are embedded and used as initial node features in a user–item bipartite graph processed by a GAT-based recommender. Model optimization relies on a hybrid objective combining Bayesian Personalized Ranking, cosine-similarity regularization, and robust negative sampling to better align semantic and collaborative signals. Finally, in the post-processing stage, an LLM-based agent re-ranks the GAT outputs using a proposed Hybrid Confidence-Weighted Binary Search Tree, and another LLM-based agent that produces natural-language justifications tailored to each user. Experiments on diverse benchmark datasets and extensive ablations demonstrate that the proposed methodology increases precision, recall, NDCG, and MAP across various values of K. In addition, the post processing step is especially effective in cold-start scenarios, consistently strengthening recommendation metrics and enhancing transparency at smaller values of K. Overall, integrating LLM-enriched representations with attention-based graph modeling enables more accurate and explainable entertainment recommendations. Full article

With the increasing integration of computer technologies into education, accurately modeling students’ knowledge mastery has become a central problem in intelligent education systems. However, existing cognitive diagnosis models often suffer from sparsity in the knowledge–item association matrix (Q-matrix) and limited model capacity, which restrict their ability to capture complex student–item interaction patterns. Collaborative filtering–based approaches further exhibit insufficient capability in modeling fine-grained cognitive relationships, leading to reduced diagnostic accuracy. To address these limitations, this study proposes a cognitive diagnosis model enhanced by an augmented knowledge association matrix, termed CAG-NCD. The proposed model refines the Q-matrix to improve the expressiveness of item–knowledge correspondences and employs nonlinear interaction functions to capture relational features in students’ response processes. Specifically, convolutional neural networks are used to extract local semantic patterns from student–item interactions, while graph convolutional networks model the global structural dependencies among knowledge points. By jointly integrating semantic and structural information, the model effectively captures complex dependency relationships. Experimental results show that CAG-NCD achieves performance improvements of 3.7% on the FrcSub dataset and 4.5% on the Math dataset, significantly reducing prediction errors and enhancing the interpretability and accuracy of cognitive diagnosis across multiple datasets. Full article

Ideological and political education (IPE) is a cornerstone of higher education in China. As IPE-related book collections expand rapidly, university libraries face a growing challenge of information overload, which hinders the accurate characterization of student reading preferences and the efficient matching of resources to demand. To address these issues, this study proposes GDNN, a practical hybrid recommendation system designed for both warm-start and cold-start scenarios. For warm-start users with historical borrowing records, we develop the PPSM-GCN framework. This framework enhances the classical graph convolutional collaborative filtering model LightGCN by integrating a novel potential positive sample mining (PPSM) strategy, which effectively mitigates data sparsity and improves the modeling of latent interests. For cold-start users without interaction history, we introduce an embedding and MLP architecture. This deep neural network learns implicit reader–book associations from reader attributes and book metadata, enabling personalized recommendations even in the absence of historical data. Experimental results demonstrate that PPSM-GCN and the embedding and MLP method achieve significant performance gains in their respective scenarios. This research provides both technical support and practical insights for the precise delivery of IPE resources and the overall enhancement of educational effectiveness in higher education. Full article

Recommender systems represent an essential infrastructure for digital platforms. To understand their evolution, we analyze 15,944 Web of Science publications (1980–2025) using bibliometric techniques, generative and transformer models for sentiment analysis and latent topic modeling. Our analysis yields three major findings. First, e-commerce recommendation research exhibits rapid growth in advanced representation techniques, with compound annual growth rates for contrastive learning (187%), graph neural networks (89%) and federated learning (72%). Second, algorithmic fairness and privacy preservation have emerged as critical research directions. Third, collaborative networks indicate a geographical shift, with Asia–Pacific regions becoming influential research hubs. The methodology integrates CAGR analysis with Latent Dirichlet Allocation (LDA, coherence score = 0.687) and BERTopic for thematic mapping and network analysis. Additionally, we employ sentiment analysis (VADER, TextBlob and a sentiment analysis pipeline from Hugging Face Transformers) and temporal heatmaps to capture research narratives. Topic modeling with LDA identifies five core themes: (1) Collaborative Filtering; (2) Machine Learning and Educational Systems; (3) Web Services and Business Applications; (4) Content and Multimedia Recommendations; (5) Graph Neural Networks and Advanced Models. BERTopic provides eight more nuanced themes based on semantics. Citation patterns follow the Pareto principle, where the top 1% of articles account for 29.1% of all citations, confirming a highly skewed impact distribution. Notably, established keywords show declining trajectories, indicating a methodological evolution toward newer, deep learning and generative AI-based paradigms. Full article

Graph Neural Networks (GNNs) have become foundational models in recommender systems due to their ability to propagate information over user–item bipartite graphs via neighborhood aggregation. Despite their empirical success, GNNs are inherently constrained by their reliance on local connectivity, which limits their ability to capture global interaction patterns, particularly in large-scale recommendation scenarios characterized by severe data sparsity. To address these challenges, we propose the Taylor Linear attention in Transformer (TLFormer), which enhances recommendation performance by enabling global attention across all user–item pairs while preserving graph structural information. Unlike existing Transformer-based recommendation approaches that focus on local attention patterns, TLFormer introduces a novel linear attention mechanism derived from the first-order Taylor approximation, allowing efficient computation of all-pair interactions. TLFormer integrates spatial topology as positional encoding while maintaining linear complexity, effectively balancing computational efficiency with model expressiveness for large-scale recommendation scenarios. Extensive experiments across multiple datasets demonstrate that TLFormer significantly outperforms state-of-the-art methods, particularly in scenarios with sparse interactions and long-tail distributions. Full article

Knowledge graphs (KGs) offer a structured and collaborative approach to integrating diverse knowledge from various domains. However, constructing knowledge graphs typically requires significant manual effort and heavily relies on pretrained models, limiting their adaptability to specific sub-domains. This paper proposes an innovative, efficient, and locally deployable knowledge graph construction framework that leverages low-rank adaptation (LoRA) to fine-tune large language models (LLMs) in order to reduce noise. By integrating iterative optimization, consistency-guided filtering, and prompt-based extraction, the proposed method achieves a balance between precision and coverage, enabling the robust extraction of standardized subject–predicate–object triples from raw long texts. This makes it highly effective for knowledge graph construction and downstream reasoning tasks. We applied the parameter-efficient open-source model Qwen3-14B, and experimental results on the SciERC dataset show that, under strict matching (i.e., ensuring the exact matching of all components), our method achieved an F1 score of 0.358, outperforming the baseline model’s F1 score of 0.349. Under fuzzy matching (allowing some parts of the triples to be unmatched), the F1 score reached 0.447, outperforming the baseline model’s F1 score of 0.392, demonstrating the effectiveness of our approach. Ablation studies validate the robustness and generalization potential of our method, highlighting the contribution of each component to the overall performance. Full article

Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorization model user–POI interaction matrices fail to fully leverage spatio-temporal information and semantic attributes, leading to weak performance on sparse and long-tail POIs. Recently, Graph Neural Networks (GNNs) have been applied by constructing heterogeneous user–POI graphs to capture high-order relations. However, they still struggle to effectively integrate spatio-temporal and semantic information and enhance the discriminative power of learned representations. To overcome these issues, we propose Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation (S²DCRec), a novel framework integrating spatio-temporal and semantic information. It employs hierarchical relational encoding to capture fine-grained behavioral patterns and high-level semantic dependencies. The model jointly captures user–POI interactions, temporal dynamics, and semantic correlations in a unified framework. Furthermore, our alignment strategy ensures micro-level collaborative and spatio-temporal consistency and macro-level semantic coherence, enabling fine-grained embedding fusion and interpretable contrastive learning. Experiments on real-world datasets, Foursquare NYC, and Yelp, show that S²DCRec outperforms all baselines, improving F1 scores by 4.04% and 3.01%, respectively. These results demonstrate the effectiveness of the dual-channel design in capturing both sequential and semantic dependencies for accurate POI recommendation. Full article

Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article

Long-term traffic flow prediction (LTFP) is crucial for intelligent transportation systems but remains challenging due to complex spatiotemporal dependencies and multi-scale temporal patterns. While recent models like Autoformer have introduced decomposition techniques, they often lack tailored mechanisms for traffic data’s unique characteristics, such as strong periodicity and long-range spatial correlations. To address this gap, we propose STLLformer, a novel spatiotemporal Transformer that establishes a seasonal-dominated, multi-component collaborative forecasting paradigm. Unlike existing approaches that merely combine decomposition with graph networks, STLLformer features: (1) a dedicated encoder–decoder architecture for separate yet synergistic modeling of trend, seasonal, and residual components; (2) a seasonal-driven autocorrelation mechanism that purely captures cyclical patterns by filtering out trend and noise interference; and (3) a low-rank graph convolutional module specifically designed to capture dynamic, long-range spatial dependencies in road networks. Experiments on two real-world traffic datasets (PEMSD8 and HHY) demonstrate that STLLformer outperforms strong baseline methods (including LSTGCN, LSTM, and ARIMA), achieving an average improvement of over 10% in MAE and RMSE (e.g., on PEMSD8 for 6-h prediction, MAE drops from 36.87 to 30.34), with statistical significance (p < 0.01). This work provides a more refined and effective decomposition-fusion solution for traffic forecasting, which holds practical promise for enhancing urban traffic management and alleviating congestion. Full article

Edge intelligence plays an increasingly vital role in ensuring the reliability of distributed microservice-based applications, which are widely used in domains such as e-commerce, industrial IoT, and cloud-edge collaborative platforms. However, anomaly detection in these systems encounters a critical challenge: labeled anomaly data are scarce. This scarcity leads to severe class asymmetry and compromised detection performance, particularly under the resource constraints of edge environments. Recent approaches based on Graph Neural Networks (GNNs)—often integrated with DeepSVDD and regularization techniques—have shown potential, but they rarely address this asymmetry in an adaptive, scenario-specific way. This work proposes Heterogeneous Graph Adaptive Augmentation (HGAA), a framework tailored for edge intelligence scenarios. HGAA dynamically optimizes graph data augmentation by leveraging feedback from online anomaly detection. To enhance detection accuracy while adhering to resource constraints, the framework incorporates a selective bias toward underrepresented anomaly types. It uses knowledge distillation to model dataset-dependent distributions and adaptively adjusts augmentation probabilities, thus avoiding excessive computational overhead in edge environments. Additionally, a dynamic adjustment mechanism evaluates augmentation success rates in real time, refining the selection processes to maintain model robustness. Experiments were conducted on two real-world datasets (TraceLog and FlowGraph) under simulated edge scenarios. Results show that HGAA consistently outperforms competitive baseline methods. Specifically, compared with the best non-adaptive augmentation strategies, HGAA achieves an average improvement of 4.5% in AUC and 4.6% in AP. Even larger gains are observed in challenging cases: for example, when using the HGT model on the TraceLog dataset, AUC improves by 14.6% and AP by 18.1%. Beyond accuracy, HGAA also significantly enhances efficiency: compared with filter-based methods, training time is reduced by up to 71% on TraceLog and 8.6% on FlowGraph, confirming its suitability for resource-constrained edge environments. These results highlight the potential of adaptive, edge-aware augmentation techniques in improving microservice anomaly detection within heterogeneous, resource-limited environments. Full article

Wikipedia is an important platform for promoting language inclusivity and sharing global knowledge. However, while languages with more resources have a lot of content, languages with fewer resources face challenges in accessibility and cultural representation. To help address this gap, we use multilingual datasets and neural graph collaborative filtering to recommend missing hyperlinks, helping to improve low-resource languages on Wikipedia. By encouraging cross-language collaboration, this method strengthens the connections and content of these languages, promoting cultural sustainability and digital inclusion. Experimental results show significant improvement in recommendation quality, with clear benefits for weaker languages. This highlights the role of recommender systems in preserving unique cultural aspects, building connections between language communities, and supporting fair knowledge sharing in a globalized world. Full article

Cloud computing has increasingly adopted multi-tenant infrastructures to enhance cost efficiency and resource utilization by enabling the shared use of computational resources. However, this shared model introduces several security and privacy concerns, including unauthorized access, data redundancy, and susceptibility to malicious activities. In such environments, the effectiveness of cloud-based recommendation systems largely depends on the trustworthiness of participating nodes. Traditional collaborative filtering techniques often suffer from limitations such as data sparsity and the cold-start problem, which significantly degrade rating prediction accuracy. To address these challenges, this study proposes a Trusted Graph-Based Collaborative Filtering Recommender System (TGBCF). The model integrates graph-based trust relationships with collaborative filtering to construct a trust-aware user network capable of generating reliable service recommendations. Each node’s reliability is quantitatively assessed using a trust metric, thereby improving both the accuracy and robustness of the recommendation process. Simulation results show that TGBCF achieves a rating prediction accuracy of 93%, outperforming the baseline collaborative filtering approach (82%). Moreover, the model reduces the influence of malicious nodes by 40–60%, demonstrating its applicability in dynamic and security-sensitive cloud service environments. Full article

To address the challenges in multi-robot collaborative SLAM, including excessive redundant computations and low processing efficiency in candidate loop closure selection during front-end loop detection, as well as high computational complexity and long iteration times due to global pose optimization in the back-end, this paper introduces several key improvements. First, a global matching and candidate loop selection strategy is incorporated into the front-end loop detection module, leveraging both LiDAR point clouds and visual features to achieve cross-robot loop detection, effectively mitigating computational redundancy and reducing false matches in collaborative multi-robot systems. Second, an improved distributed robust pose graph optimization algorithm is proposed in the back-end module. By introducing a robust cost function to filter out erroneous loop closures and employing a subgraph optimization strategy during iterative optimization, the proposed approach enhances convergence speed and solution quality, thereby reducing uncertainty in multi-robot pose association. Experimental results demonstrate that the proposed method significantly improves computational efficiency and localization accuracy. Specifically, in front-end loop detection, the proposed algorithm achieves an F1-score improvement of approximately 8.5–51.5% compared to other methods. In back-end optimization, it outperforms traditional algorithms in terms of both convergence speed and optimization accuracy. In terms of localization accuracy, the proposed method achieves an improvement of approximately 32.8% over other open source algorithms. Full article

This study introduces APGCN-CF, an innovative graph convolutional network-based framework designed to address the distinctive challenges of minor agricultural product recommendation in rural e-commerce environments. The framework synergistically integrates graph convolutional networks (GCNs) with collaborative filtering (CF), providing an effective solution to critical challenges in minor agricultural product recommendations. APGCN-CF implements a multi-module architecture encompassing graph structure construction, convolution-based feature learning, user similarity-based candidate generation, and preference probability-driven precise recommendation. Comprehensive experimental evaluations conducted on real-world minor agricultural product datasets, featuring scallions and garlic as representative cases, demonstrate that APGCN-CF consistently outperforms existing state-of-the-art baseline methods across multiple performance metrics, including precision, recall, and F1-score. Notably, the framework exhibits superior performance in challenging scenarios characterized by data sparsity and cold-start conditions through its effective feature extraction and relationship modeling capabilities. Through deep integration of user preference features, APGCN-CF enables the dynamic generation of highly personalized recommendation lists, providing robust decision support for optimizing minor agricultural product sales. This research not only advances the theoretical landscape of recommendation systems but also delivers a practical solution tailored for personalized recommendation within rural e-commerce platforms. Full article

In contemporary society, individuals are inundated with a vast amount of redundant information, and recommendation systems have undoubtedly opened up new avenues for managing irrelevant data. Graph convolutional networks (GCNs) have demonstrated remarkable performance in the field of recommendation systems by iteratively performing node convolutions to capture information from neighboring nodes, thereby enhancing recommendation efficacy. However, most existing models fail to distinguish the importance of different nodes, which limits their performance. To address this issue, we propose the asGCL model. To mitigate the prevalent issue of popularity bias and to learn more uniform embedding representations, we have integrated a lightweight contrastive learning module into our model. Finally, extensive experiments conducted on four real-world datasets demonstrate the effectiveness of our model. Notably, on the Amazon-Books dataset, our asGCL model achieved improvements of 4.21% and 8.74% in recall@20 and NDCG@20, respectively. Full article