LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Xiao, Yong; Liu, Jianxun; Kang, Guosheng; Cao, Buqing

doi:10.1109/tnsm.2021.3084739

Cited by 12 publications

(6 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiao et al. (Xiao et al. , 2021) introduce LDNM, a Web service classification framework that combines structured and unstructured features using deep fusion techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, by incorporating residual learning, the model can achieve deeper depths, facilitating the extraction of richer features from the graph data. Xiao et al (Xiao et al, 2021) introduce LDNM, a Web service classification framework that combines structured and unstructured features using deep fusion techniques. It uses two methods for document representation: LDA for topic distribution and Doc2vec for neural-network-based embeddings.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Graph structure estimation neural network-based service classification

Li,

Xie,

Cao

et al. 2024

IJWIS

Self Cite

View full text Add to dashboard Cite

Purpose With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model. Design/methodology/approach First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph. Findings The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification. Originality/value This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.

show abstract

“…Xiao et al. (Xiao et al. , 2021) introduce LDNM, a Web service classification framework that combines structured and unstructured features using deep fusion techniques.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Graph structure estimation neural network-based service classification

Li,

Xie,

Cao

et al. 2024

IJWIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, numerous graph convolutional networks (GCNs) have been proposed [8][9][10][11][12]. GCNs can integrate feature information from local graph neighborhood, which have been demonstrated to be powerful for graphical representation [13][14][15]. Neural Graph Collaborative Filtering (NGCF) [13] exploits the user-service graph structure and integrates the users and services interactions into the embedding process.…”

Section: Introductionmentioning

confidence: 99%

“…PinSage [ 14 ] designs an efficient random walk method with graph convolutions to generate node embedding that incorporates both graph structure and node information. LDNM [ 15 ] transforms each service document into feature vectors by using LDA and Doc2vec, then applies Node2vec and MLP neural network for Web Service Classification.…”

Section: Introductionmentioning

confidence: 99%

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Xia

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.

show abstract

“…For instance, there are only 3,261 Web services in 2011 released on the ProgrammableWeb platform, while there are more than 30,000 ones in 2020. Many Web services share the same or similar functionality but provide different QoS (Quality of Service) [1]. With SOA (Service Oriented Architecture) techniques, the coarse-grained, loosely coupled Web services can be composed into complex applications or software systems [2].…”

Section: Introductionmentioning

confidence: 99%

QoS-Centric Web service Recommendation with Personalized Diversity

Kang

Liang

Liu

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

With the popularity and widespread adoption of SOA (Service Orient Architecture), the number of Web services has increased exponentially on the Internet. Meanwhile, as the cost of Internet applications decreases, users tend to use online Web services for their daily business and software development needs. Under such a background, recommending desirable Web services to users which meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue owing to the QoS preference is usually hard to provide for users, i.e., QoS preference is uncertain. To solve the problem, some recent works try to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the former QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the user’s service invocation history and provides a Web service recommendation algorithm, named personalized DPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experiment results show that the proposed approach can provide personalized and diversified Web services on the premise of ensuring the overall accuracy of the recommendation results.

show abstract

LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Cited by 12 publications

References 44 publications

Graph structure estimation neural network-based service classification

Graph structure estimation neural network-based service classification

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

QoS-Centric Web service Recommendation with Personalized Diversity

Contact Info

Product

Resources

About