Web Service Recommendation Technology Based on Knowledge Graph Representation Learning

Abstract

Abstract This paper proposed a recommendation algorithm based on knowledge graph representation learning (RABKGRL).The algorithm embeds the entities and relationships of the knowledge graph into the low-dimensional vector space. The relationship information of services is incorporated into the recommendation algorithm by calculating the distance between the service entities. The association between services that is not considered when using the collaborative filtering algorithm can be supplemented, and the recommendation effect is enhanced. 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.