Web Service Recommendation via Combining Bilinear Graph Representation and xDeepFM Quality Prediction

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 func-tion 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 repre-sentations from service description documents. Then, it constructs the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggre-gator is used to model the pairwise interactions between neighbor service nodes. Integrated with the traditional weighted sum ag-gregator, 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 mul-ti-dimensional QoS attributes. Aiming at the Web services in the service cluster, it utilizes xDeepFM to model and mine the com-plex 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 com-pared with the other ten methods, the proposed approach has better performance in terms of Accuracy, Recall, F1, Logloss, and AUC, and has better performance in classification and recom-mendation.