Knowledge Graph-based Recommendation Engine: The Review

With the rapid growth of digital information and the increasing complexity of user preferences, recommender systems have become essential in various application domains. Knowledge graph-based recommendation methods have emerged as a promising approach to enhance the accuracy and interpretability of recommendations. This research study explores the landscape of knowledge graph-based recommendation methods and examine benchmark datasets from the perspectives of different application scenarios. Here, the application scenarios are categorized into e-commerce, social media, content-based, and cross-domain recommendations. This study has analyzed the challenges and opportunities that arise in each scenario and discuss the corresponding knowledge graph-based recommendation techniques. Furthermore, this study investigates the existing benchmark datasets specifically designed for knowledge graph-based recommendation, highlighting their characteristics, strengths, and limitations. By providing an in-depth analysis of knowledge graph-based recommendation methods and benchmark datasets, this review paper serves as a valuable resource for researchers and practitioners working in the field, aiding in the development and evaluation of effective recommendation systems tailored to specific application scenarios.