Deep knowledge-aware framework for web service recommendation

Dang, Depeng; Chen, Chuangxia; Li, Haochen; Yan, Rongen; Guo, Zixian; Wang, Xingjian

doi:10.1007/s11227-021-03832-2

Cited by 25 publications

(14 citation statements)

References 42 publications

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“…Graph representation learningbased recommendation system [43] encodes each node into a latent representation and then analyzes the complex relations between them. Another class of recommendation systems are built on the knowledge graph (KG) to explore latent relations among users or items connected as a heterogeneous graph [44].…”

Section: B Graph Learning-based Recommendation Systemsmentioning

confidence: 99%

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

Tian¹,

Jiang²,

Li³

et al. 2023

Preprint

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Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death. This paper presents a Decision Support System, called DSSDDI, based on drug-drug interactions to support doctors prescribing decisions. DSSDDI contains three modules, Drug-Drug Interaction (DDI) module, Medical Decision (MD) module and Medical Support (MS) module. The DDI module learns safer and more effective drug representations from the drugdrug interactions. To capture the potential causal relationship between DDI and medication use, the MD module considers the representations of patients and drugs as context, DDI and patients' similarity as treatment, and medication use as outcome to construct counterfactual links for the representation learning. Furthermore, the MS module provides drug candidates to doctors with explanations. Experiments on the chronic data collected from the Hong Kong Chronic Disease Study Project and a public diagnostic data MIMIC-III demonstrate that DSSDDI can be a reliable reference for doctors in terms of safety and efficiency of clinical diagnosis, with significant improvements compared to baseline methods. Source code of the proposed DSSDDI is publicly available at https://github.com/TianBian95/DSSDDI.

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Section: B Graph Learning-based Recommendation Systemsmentioning

confidence: 99%

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

Tian¹,

Jiang²,

Li³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Later, they learn implicit low-dimensional embeddings of entities in the knowledge graph from truncated random walks [24]. Dang et al propose a deep knowledge-aware approach for service recommendation that learns text and knowledge graph embeddings and use an attention mechanism to model tags [25]. Nguyen et al learn the context of mashups and services by using Doc2Vec and enhance the traditional PMF with attentional mechanism to weight their latent features [26].…”

Section: Related Workmentioning

confidence: 99%

Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development

Gao¹,

Wu²

2022

Applied Sciences

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With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available.

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“…Dang et al 29 proposed a web service recommendation system by a deep knowledge‐aware approach that introduces knowledge graphs and knowledge representations altogether. They solved the data‐sparse problem and optimized the user's feature representation.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

Gnanasekaran

Chinnasamy

Parasuraman³

2022

Concurrency and Computation

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Quality of service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, performing a highly accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, a hybrid deep learning technique is proposed to predict future QoS parameters. Initially, the data is collected from the WSDream data repository, however, the collaborating filtering (CF) technique is used to pre-process the collected data. Accordingly, the filtering technique resolves missing or undermined values and cleans the node. Additionally, this pre-processing technique reduces the error and promotes prediction efficiency. Subsequently, data clustering is performed by introducing a modified k-medoids algorithm, which groups the user services into different clusters. Accordingly, a future QoS is predicted based on the proposed hybrid deep learning method like convolutional neural network-long short-term memory. The proposed method is implemented using Python software and it evaluates response time, throughput, root mean square error, and mean absolute error (MAE). The proposed method is compared with the existing collaborative filtering-k medoids, support vector regression, self-exciting threshold auto-regressive, genetic guided clustering algorithm algorithms, and novel deep learning-based hybrid approach. The result of the proposed method presents higher accuracy and low consumption time. Therefore, the performance of the proposed method is 6% higher than the other existing methods. Accordingly, the MAE, mean absolute percentage error, mean absolute scaled error, and root mean squared error of the proposed method produces 7%, 7.5%, 2%, and 1.5% higher performance than the other existing methods.Subsequently, the precision and recall of the proposed method are approximately 3.5% and 3% high than the other state of art methods. The experimental results highlight that the viability of the proposed approach contrasted with the regular methodologies in real-time. Consequently, by introducing this proposed method the QoS prediction performance is improved and it provides higher-quality solutions.

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Deep knowledge-aware framework for web service recommendation

Cited by 25 publications

References 42 publications

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development

Analyzing theQoSprediction for web service recommendation using time series forecasting with deep learning techniques

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