KG2Rec: LSH-CF recommendation method based on knowledge graph for cloud services.

With the rapid development of cloud computing paradigm and mobile devices, the number of cloud services and user interest data are growing explosively. It becomes harder for users to find suitable cloud services within satisfying response time. Thus a suitable cloud service automatic recommendation system is needed to solve the above problem. In this work, we propose KG2Rec, a novel recommendation method based on Knowledge Graph to provide users with appropriate recommendations to meet their real-time needs. The property-specific knowledge graph model is designed to model user–item and item–item bipartite relations. Based on the property-specific knowledge graph model, property-specific user–item relation features can be mined from it. Then the user–item relation features will be integrated into an improved collaborative filtering algorithm based on the local sensitive hash technique for Top-N item recommendation. Finally, we evaluate the proposed method in terms of Top-N recommendation on the MovieLens dataset, and prove it outperforms numbers of state-of-the-art recommendation systems. In addition, we prove it has well performance in term of long tail recommendation and big data processing. [ABSTRACT FROM AUTHOR]