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Most existing Web service recommendation models based on machine learning do not fully consider the high-order features interaction between users and services and with poor interpretability. In this paper, an Interpretable Web Service Recommendation model based on Disentangled Representation Learning (WSR-DRL) is proposed. First of all, to make full use of the service description information to improve the accuracy of Web service recommendation, the features representation of service name is obtained by using BERT model, and the local and global features representation of service description information is further obtained by combining 2-D CNN and Bi-LSTM. Then the disentangled convolution neural network is used to generate the high-order interaction features between users and services, and the neighborhood routing algorithm is used to mine the latent factors in these features. That improves the accuracy of Web service recommendation and make it interpretable. Finally, in order to verify the effectiveness of the model, several groups of experiments are carried out on real data sets. The experimental results show that compared with latest models such as DMF, DeepFM, DKN, GCMC, NDCG model and WSR-MGAT model, the WSR-DRL model proposed in this paper shows better performance on Precision@10, Recall@10, F1@10 and NDCG@10 evaluation metrics. [ABSTRACT FROM AUTHOR]

Existing Web services recommendation models suffer from matrix sparsity and cold-start problems due to the limited number of user-service interactions and too little contextual information, so this paper introduces knowledge graph as auxiliary information to Web service recommendation to alleviate matrix sparsity and cold-start problems. And we propose the Web services recommendation based on Metapath-guided Graph Attention Network Model (WSR-MGAT) to fully exploit the structural information of the knowledge graph to improve the recommendation accuracy. Specifically, WSR-MGAT uses graph embedding method to obtain the initial embedding of entities and relationships. In order to obtain more closely related neighbors, we propose to use a distance-aware path sampling method to extract meta-path instances with closer relationships. Previous knowledge graph-based Web service recommendations do not make full use of the rich interaction information, which may lead to limited performance. To address this problem, this paper uses meta-paths to guide nodes to recursively aggregate higher-order neighbor information and use an attention mechanism to distinguish the importance of neighbors. Meanwhile, the semantic information of nodes under different meta-paths is fused to obtain a more comprehensive embedding of nodes. To verify the performance of the model, we use the real data crawled from the ProgrammableWeb platform to conduct multiple groups of experiments. The experimental results show that the WSR-MGAT improves over the strongest baselines w.r.t. Prec@10 by 4.6%; Recall@10 by 3.8%; NDCG@10 by 4.2% and F1@10 by 4.9%. [ABSTRACT FROM AUTHOR]

Using semantic information can help to accurately find suitable services from a variety of available (different semantics) services, and the semantic information of Web services can be described in detail in a Web service knowledge graph. In this paper, a Web service recommendation algorithm based on knowledge graph representation learning (kg-WSR) is proposed. The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. By calculating the distance between service entities in low-dimensional space, the relationship information of services which is not considered in recommendation approaches using a collaborative filtering algorithm is incorporated into the recommendation algorithm to enhance the accurateness of the result. The experimental results show that this algorithm can not only effectively improve the accuracy rate, recall rate, and coverage rate of recommendation but also solve the cold start problem to some extent. [ABSTRACT FROM AUTHOR]