The number of online services is rapidly increasing due to the increased adoption of services-oriented technology. In this context, recommendation systems can provide high-quality Web services that meet Mashup developers’ expectations. The use of different kinds of auxiliary information in recommendation systems is commonplace. They enrich recommendation systems so that they can make relevant recommendations. Yet, knowledge graph-based service recommendation usually only considers the textual semantic information of the service and ignores the importance of discrete attribute information of the service for service recommendation. This may lead to the inability to comprehensively capture the multidimensional characteristics of services, thus affecting the accuracy and reliability of recommendations. To this end, this paper proposes a Web service recommendation method for Mashup creation that exploits pre-training representation and knowledge graphs as auxiliary information. Firstly, it uses the neural factorization machines and Doc2Vec to obtain the text semantic representation and the discrete attribute representation of Web services respectively. Secondly, it combines the text semantic representation and the discrete attribute representation to generate the pre-training representation as the input of knowledge graph convolutional networks. Thirdly, it constructs the Web services knowledge graph using Mashups, Web services, and related information and learns the preferences of Mashup developers and higher-order structural relations between Web services using knowledge graph convolutional networks to complete Web service recommendations. Finally, the proposed method is compared to the baselines, i.e., feature interaction-based (LR, FM, FFM, and NFM), KG-based (RippleNet and KGCN), and Doc2Vec for entity representation-based (DKGCN) Web service recommendation methods, using a real-world dataset from ProgrammableWeb. The experimental results show that the proposed method significantly improves the quality of recommendation in terms of the accuracy, recall, and Micro-F1.

With the increasing number of Web services, how to provide developers with Web services that meet their Mashup requirements accurately and efficiently has become a challenging problem. Therefore, focusing on the problem of “recommending appropriate services to build high-quality Mashup applications”, this paper proposes a Web service recommendation method via combining bilinear graph attention representation and xDeepFM (eXtreme Deep Factorization Machine) quality prediction. This method is based on content and structure-oriented service function classification and combines it with the service invocation prediction based on multi-dimensional quality attributes. Firstly, it uses the Word2Vec model to learn the latent semantic representations from service description documents. Then, it constructs the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes. Integrated with the traditional weighted sum aggregator, a bilinear graph neural network (BGNN) with stronger node representation ability is constructed. It exploits BGNN to calculate the representation of service nodes in the network and divides services into different functionality clusters. Finally, the high-quality representation results are combined with multi-dimensional QoS attributes. Aiming at the Web services in the service cluster, it utilizes xDeepFM to model and mine the complex interactions between Web services” features, and predict and rank the invocation scores of Web services. The experimental results on the real dataset of ProgrammableWeb show that compared with the other ten methods, the proposed approach has better performance in terms ofAccuracy,Recall,F1,Logloss, andAUC, and has better performance in classification and recommendation.

The rapid growth in the number and diversity of Web service, coupled with the myriad of similar Web service in functionality, makes it challenging to find most suitable Web service for users to accelerate and accomplish Mashup development. Therefore, this article proposes a Web service recommendation method via integrating heterogeneous graph attention network representation and FiBiNET (Feature Importance and Bilinear feature Interaction NETwork) score prediction. In this method, first, a heterogeneous information service network is constructed by using composite service information, atomic service information, and their respective attribute information. Second, the meta-paths are defined according to different semantic information and service similarity matrixes are built by using commuting matrix and meta-path-based similarity measurement technology. A two-layer attention model is designed to calculate the node level attention and meta-path-level attention of the services respectively, and generate the feature representation of Web service. Third, for the Web services in the service cluster, combining their feature representations with multi-dimensional QoS attributes, the FiBiNET is exploited to dynamically learn the importance of features and complex feature interactions, and predict the score of Web services. Finally, the experiments are performed on the real Web service dataset. The experimental results show that the proposed method is better than the other nine methods in terms of accuracy, recall, F1, and AUC, and achieves better classification and recommendation quality.

SUMMARY As more and more companies and organizations encapsulate and publish their business data or resources to the Internet in the form of APIs, the number of web APIs has grown exponentially. For this reason, it has become challenging to quickly and effectively find web APIs from such a large‐scale web API collection, which meet the requirements of mashup developers. To this end, this article focuses on recommending suitable web APIs to build high‐quality mashups by classifying and integrating content‐oriented service functionality with service invocation prediction. The proposed web API recommendation method for mashup development uses graph attention representation and DeepFM quality prediction. First, it uses the web API composition and shared annotation relationships to construct a web API relationship network. Second, it applies the self‐attention mechanism to compute the attention coefficients of different neighboring nodes in the web API relationship network. So, for a specific web API node, the weighted sum of the importance of its neighboring nodes and features characterizes that web API node. Doing so ensures that the service can be divided more accurately into different functional clusters via high‐quality characterization. Third, for the web APIs in a cluster, the high‐quality representation results are combined with multidimensional quality of service attributes. It employs the DeepFM to model and mine complex interaction relationships between features and subsequently predict and rank the invocation scores of web APIs. Finally, experiments are compared and analyzed on real‐world web API datasets. It can be seen from the results of several groups of comparative experiments that the proposed method outperforms other nine baseline methods on accuracy, recall, F1, DCG, and AUC and achieved a good classification accuracy and recommendation effect.

Service classification helps to improve the efficiency of service discovery. Previous methods mainly focus on homogeneous graph-based service classification. However, due to the heterogeneity of service data in the real world, these methods cannot deal with many types of nodes and edges in service relationship network well, and lack the usage of rich semantic information. The emergence of heterogeneous graph attention network can effectively solve the problems, because it can more completely and naturally extracts the relationships and nodes from the service relationship network, and well distinguishes the importance of neighbor nodes and meta paths. Therefore, this paper proposes a heterogeneous graph attention network-enhanced Web service classification method. In this method, firstly, a heterogeneous information service network is constructed by using composite service information, atomic service information and their attribute information. Then, the meta path is defined according to different semantic information, and the similarity matrix of service is constructed by using the commuting matrix and the similarity measurement technology based on meta path. Finally, a two-layer attention model is designed to calculate the node-level attention and meta path-level attention of the service, so as to obtain the node-level representations and meta path-level representations of the services, and generate more representative embedding features of services for achieving more accurate service classification. Finally, the experimental results on real datasets of ProgrammableWeb show that our method is better than GAT, GCN, Metapath2Vec, Node2Vec, BiLSTM and LDA in terms of precision, recall and macro F1, and improves the accuracy of Web service classification.

With the rapid development of Internet, the number of Web services is increasing sharply, which makes it more difficult for Mashup developers to find suitable Web services. Nowadays, there are numerous methods to improve Web service recommendation, but it is still a challenging problem to recommend Web services with both good accuracy and satisfying diversity. Collaborative filtering is a common algorithm in recommendation system, but it often faces serious cold start and sparsity problems. To alleviate the above problems, this paper proposes a Web service recommendation method based on knowledge graph convolutional network and Doc2Vec. First of all, it constructs the knowledge graph of Web services based on the additional information such as the categories, developers, scope of application of Web services, and adopts knowledge graph convolutional networks to mine the higher-order relationship between Web service and the preference information of Mashups. Secondly, it employs Doc2Vec to mine the semantics of Web service description documents, and integrates the Mashup preference information and the Mashup semantic information in the training process, so as to predict Web services needed for Mashup development. Finally, the experiment is conducted on the latest Programmable Web dataset and the experimental results show that the recommended performance of the proposed method is better than that of FM, NCF, CKE, RippleNet, KGCN.