With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.
DAPath: Distance-aware knowledge graph reasoning based on deep reinforcement learning