关键词: 标签推荐, 多通道, 注意力机制, Web服务

Abstract: The quality of service representation vector is the key factor affecting the accuracy of Web service category label recommendation.Most of the existing methods face the problems of incomplete semantic expression and low accuracy when generating service representation vector,which reduces the recommendation accuracy of service category label.Therefore,a Web service category label recommendation method that integrated multi-channel semantic information and attention mechanism was proposed.The embedding of feature words in the service description text was generated using RoBERTa model,and the semantic information extraction channel for feature words of different granularity was established.Then,a global semantic extraction model named FRASRU with fast regular approximate attention mechanism was constructed to realize the fast fusion of feature words' semantic features and global semantic features.Finally,the service representation vector with multi-channel feature fusion was input into the pre-trained sigmiod classifier to realize category label recommendation.Experiments showed that the proposed method was superior to the comparison models and classification methods in the metrics of F1,Recall and Precision,which had good classification performance.

Key words: label recommendation, multi-channel, attention mechanism, Web services