Web APIs have become a fundamental paradigm in the Web 4.0 era, with mashup services emerging as a transformative technology that combines multiple APIs to create comprehensive services. However, existing approaches exhibit two significant limitations: overlooking the quality and completeness of recommendation contexts of new mashup requirements, and failing to effectively extract high-quality collaborative features from multi-perspective service relationships. To address these limitations, we propose LMSR, a novelLLM-enhancedMulti-perspectiveService Feature Learning framework for Web APIRecommendation. LMSR first leverages general-purpose LLM to refine and encode the original requirement descriptions, and employs a Mixture of Service Experts (MoSE)-based context prediction model to precisely predict service information relevant to new requirements, establishing a comprehensive and high-quality recommendation context for mashup requirements. Furthermore, by integrating the predicted recommendation contexts into the LLM through fine-tuning, LMSR effectively extracts collaborative features from multi-perspective service relationships between mashup requirements and APIs, ultimately achieving precise Web API recommendation. Comprehensive experiments on real-world datasets demonstrate that LMSR significantly outperforms 11 baseline approaches across precision, recall, F1-score, and NDCG, validating its effectiveness in Web API recommendation. The codes are available athttps://scdm-shu.github.io/codes/LMSR.zip.

While Large Language Models (LLMs) show promise for Text-Attributed Graphs (TAGs) learning, their deployment is hindered by computational demands. Graph Neural Networks (GNNs) are efficient but struggle with TAGs' complex semantics. We propose LinguGKD, a novel LLM-to-GNN knowledge distillation framework that enables transferring both local semantic details and global structural information from LLMs to GNNs. First, it introduces TAG-oriented instruction tuning, enhancing LLMs with graph-specific knowledge through carefully designed prompts. Next, it develops a layer-adaptive multi-scale contrastive distillation strategy aligning LLM and GNN features at multiple granularities, from node-level to graph-level. Finally, the distilled GNNs combine the semantic richness of LLMs with the computational efficiency of traditional GNNs. Experiments demonstrate that LinguGKD outperforms existing graph distillation frameworks, the distilled simple GNNs achieve comparable or superior performance to more complex GNNs and teacher LLMs, while maintaining computational efficiency. This work bridges the gap between LLMs and GNNs, facilitating advanced graph learning in resource-constrained environments and providing a framework to leverage ongoing LLM advancements for GNN improvement.

Edge service deployment has attracted significant attention in recent years, aiming to optimize service placement on edge servers while satisfying diverse requirements. However, existing approaches often overlook the influence of geographical contexts on service demands, where user needs vary significantly across regions with distinct characteristics. They also fail to account for differences between direct responses and multi-hop forwarding in edge network topology, leading to unsatisfactory edge service deployment strategies. To this end, we formulate the Points of Interest-Based Edge Service Deployment (POI-ESD) problem with topology-aware optimization, integrating POI attributes and spatial distributions while incorporating edge network topology to enhance service placement. By proving the$\mathcal{N}P$-hardness of POI-ESD problem, we propose a novel graph-encoded genetic algorithm, MTGA, to efficiently generate high-quality deployment strategies. It ensures strategic placement of edge services in regions that best match user demands, improving the service utilization and satisfiability for edge users. Extensive experiments on a real-world dataset combining Shanghai Telecom and Baidu Maps POI data demonstrate that MTGA significantly outperforms existing competing approaches, achieving superior performance of edge service deployment.

Connected vehicles due to the high mobility and dynamic network topologies of connected vehicles require accurate QoS that includes high throughput and low latency to assess satisfactory QoE. Existing methods mainly focus on centralized QoS prediction while paying little attention to distributed mobile QoS prediction, making it challenging to protect user privacy information when invoking Web services. Moreover, even though some advanced centralized methods can be transformed into federated architectures, they often face difficulty in capturing latent feature representations of users and services and learning personalized prediction layers between them due to the heterogeneity of the QoS dataset. To address the above issues, we propose a novel framework for distributed QoS prediction, called Combining Personalized Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction (FHR-DQP) . FHR-DQP adopts the federated averaging (FedAvg) to aggregate location-aware residual shared feature information across all clients. Additionally, a hypernetwork is leveraged to generate personalized networks for user-service QoS prediction in each client. These components are integrated as a hybrid framework that performs training using a federated approach and makes personalized QoS predictions within each client. Extensive experiments are conducted on a real-world benchmark QoS dataset called WS-DREAM, containing nearly 2,000,000 historical QoS invocation records. Compared with both centralized and federated competing baselines, the results demonstrate that FHR-DQP achieves the highest performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.

Recently, the edge data caching (EDC) problem has received much attention. It aims to appropriately cache data on edge servers. Existing EDC approaches suffer from a series of limitations. First, they often overlook the diverse characteristics of data, including caching costs and latency preferences. In reality, different types of data vary in size and require different storage resources for caching. The impact of specified latency preferences of edge users for different data on the quality of experience should be considered in the EDC problem. Second, the temporal dynamics of edge users’ data requests and distributions have been insufficiently addressed. To overcome these limitations systematically, this paper focuses on the problem of temporal-aware edge data caching with specified latency preference (TEDC). We first formulate the TEDC problem and transform it into an optimization problem with multiple objectives and global constraints and prove its ${\mathcal{N}}{\mathcal{P}}$-hardness. Then, we propose an optimal approach named TEDC-IP to solve this TEDC problem with the Integer Programming technique and a heuristic algorithm named TEDC-A for finding approximate solutions to large-scale TEDC problems efficiently. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of our approach. The results demonstrate that TEDC-IP and TEDC-A significantly outperform state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

With the surge in Web service users, user profiling has become increasingly prominent in personalized service recommender system. Graph Neural Networks (GNNs) has emerged as a key technology for user feature extraction. However, these methods mostly focus on modeling pairwise interaction relationships by type and overlook the high-order interaction relationships and deep semantic correlations. Moreover, GNNs’ limited receptive fields restrict their capacity to capture user high-order features effectively. To address these issues, we propose a novel framework for advanced user profiling named Heterogeneous Interaction Graph Transformer (HIGT). Firstly, HIGT constructs a weighted heterogeneous interaction graph from historical user-service interactions, using edge types for interaction modes and weights for their frequency. Secondly, it uses a Transformer to extract high-order semantic attribute correlations and enhance global understanding through self-attention, while proposing a structure-enhanced attention mechanism to incorporate the graph structure into the Transformer architecture for extracting high-order interaction features of users. This dual high-order feature learning method provides deeper insight into users’ preferences for Web services. Extensive experiments on two real-world e-commerce service datasets reveal that HIGT brings a significant performance boost compared with competing models for user profiling.

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.