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.

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.

Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%.

Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimization, which causes privacy breaches. While some federated learning-based methods consider user privacy in a distributed way, they either directly upload local trained parameters or use simple encryption for global aggregation at the central server, thus failing to truly protect user privacy. Secondly, existing federated learning-based methods neglect distributed user-service topology and latent behavior-attribute correlations, compromising QoS prediction accuracy. To address these limitations, we propose a novel framework namedPrivacy-EnhancedFederated ExpandedGraphLearning (PE-FGL) for secure QoS prediction. It first conducts user-service expansion on the invocation graph with advanced privacy-preserving techniques, upgrading first-order local QoS invocations to high-order interaction relationships. Then, it extracts hybrid features from the expanded invocation graph via deep learning and graph residual learning. Finally, a two-layer secure mechanism of federated parameters aggregation is designed to enable collaborative learning among users through local parameter segmentation and global aggregation, achieving effective and secure QoS prediction. Extensive experiments on WS-DREAM demonstrate effective QoS prediction across multiple metrics while preserving privacy in user-service 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.

The proliferation of homogeneous web services has necessitated the task of predicting vacant Quality of Service (QoS) for service-oriented downstream tasks. Existing approaches primarily focus on user-service invocations without considering temporal factors, limiting their applicability in QoS fluctuations over time. Moreover, some investigations are conducted to predict temporally missing QoS, which still suffers from two limitations. First, time-aware collaborative filtering (CF) approaches fail to well capture continuous temporal changes, which lowers the performance of time-aware QoS prediction. Second, they have paid less attention to the high sparsity of user-service QoS invocations across sequentially multiple time slices, which affects the calculation of temporal average QoS, thereby further reducing the accuracy of time-aware QoS prediction. To effectively mine the continuous temporal variations and solve the high sparsity of user-service QoS invocations, we propose a novel time-aware QoS prediction approach named Temporal Reinforced Collaborative Filtering (TRCF). We design temporal reinforced RBS and PCC to improve similarity evaluation that leads to better calculation of temporal average QoS and deviation migration for predicting time-aware QoS. We evaluate TRCF on a large-scale real-world temporal dataset WS-DREAM across 64 time slices and the results demonstrate its superior performance in time-aware QoS prediction, both under relatively dense and extremely sparse QoS situations.

QoS prediction plays an important role in service-oriented downstream tasks. However, most of current state-of-the-art QoS prediction approaches suffer from two limitations. First, traditional approaches typically require collection of user-service historical QoS invocations centrally in order to improve QoS prediction accuracy, which poses a threat to user data privacy. Second, although few of the recent approaches take into account data protection when predicting QoS values, they still cannot effectively capture user-service complex nonlinear invocation relationships, significantly influencing the performance of QoS prediction. To address these two issues, we propose a novel framework of data-protected QoS prediction called Federated Residual Ladder Network (FRLN), which ensures user data protection and effectiveness of predicting missing QoS values. It initially leverages our designed Residual Ladder Network (RLN) to extract latent features of users and services from both low and high dimensional spaces. Then, local QoS prediction models are collaboratively trained by personalized federated learning with the consideration of data heterogeneity. Extensive experiments have been conducted on a real-world large-scale dataset called WS-DREAM, which consists of 5825 Web services from 74 regions and 339 users from 31 regions comprising a total number of 1,974,675 user-service QoS invocations. Experimental results demonstrate the effectiveness of FRLN in multiple evaluation metrics. While the proposed FRLN framework marks a significant step forward for QoS prediction in machine learning, ongoing advancements in ML techniques and expanded datasets are essential for further enhancing its precision and applicability in real-world scenarios.

In service recommender system, graph neural networks (GNNs) perform message passing through diffusion mechanism based on user-service relationship graph. However, existing GNN-based service recommendation models suffer from two limitations: ❨1❩ message passing is only carried out at firstorder neighbors, as higher-order may cause over-smoothing phenomenon, confining feature propagation in GNNs; and ❨2❩ due to sparse and noisy interactions, the distribution of embedding vectors is nonuniform in the latent space, resulting in unsatisfactory performance for downstream applications. To this end, we propose a fixed global graph diffusion view that is independent of the original user-service observed local view to form a multi-view learning by building contrastive learning (CL) relationship, named as Multi-View Graph Contrastive Learning (MVGCL). Specifically, it enhances the capability of message passing through constructed local and global multi-view graphs, and alleviates the sparse and noisy influences by performing intra-CL within local/global view and inter-CL between multi-view to obtain a more uniform distribution of user and service node representations. Extensive experiments are conducted on three benchmark datasets within different scales, and the results demonstrate that our proposed MVGCL can remarkably outperforms state-of-the-art competing baselines on various evaluation metrics.

With the overwhelming explosion of Web services, how to effectively predict unknown QoS has become a key issue of differentiating large-scale similar or functionally equivalent Web services. However, current state-of-the-art QoS prediction approaches based on deep learning still suffer from two deficiencies. First, they mainly focus on predicting vacant QoS in a centralized manner and scarcely take into account distributed QoS prediction, which makes difficult to protect the privacy information of users invoking Web services. Second, they have ignored the hierarchical collaborative relationship to better extract latent features of users and services, reducing the accuracy of QoS prediction. To address these two issues, we propose a novel framework calledFederatedHierarchicalClustering forDistributedQoSPrediction(FHC-DQP). It collaboratively performs distributed federated training on independent users’ QoS invocations, and then the extracted federated users’ private features are fed to clustering algorithm for partitioning them into a set of clusters. By iteratively federated hierarchical clustering, users are fine-grained partitioned together and those users within the same cluster have stronger collaborative relevance for more effectively learning the latent features of users and services leading to the performance improvement of distributed QoS prediction, where contextual-aware deep neural network is designed for personalized QoS prediction. Extensive experiments are conducted based on a public real-world benchmarking dataset called WS-DREAM with almost 2,000,000 user-service historical QoS invocations. Compared with both centralized and federated competing baselines, the results demonstrate FHC-DQP receives superior performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.

How to accurately predict vacant QoS has become a fundamental issue for service-oriented downstream tasks. However, most QoS prediction approaches based on model learning fail to discriminatively capture the latent feature representations of a user and a service, since they either leverage the shallow neural network such as MLP or take advantage of insufficient location information. Moreover, collaborative relationships of similar neighborhood have not been fully taken into account together with prediction model learning. To address these issues, we propose a novel framework for adaptive QoS prediction named Neighborhood-based Collaborative Residual Learning (NCRL). Location-aware two-tower deep residual network is designed to achieve neural QoS prediction by extracting latent features of users and services, which are fed to generate similar neighborhood for collaborative prediction based on historical QoS invocations. They are integrally combined to perform adaptive QoS prediction. Extensive experiments are conducted based on a large-scale real-world QoS dataset called WS-DREAM with almost 2,000,000 historical QoS invocations. The results indicate that NCRL can remarkably outperform state-of-the-art competing baselines.