Implementing Knowledge Graphs and Ontologies For AI-Powered Recommendation System

In recent years, the proliferation of options available to users has made it increasingly challenging for individuals to identify and select items that align with their interests. This abundance of information has similarly posed significant difficulties for organizations tasked with extracting meaningful insights and providing relevant recommendations from vast datasets. Recommender systems (RS) have emerged as a crucial solution to this challenge, aiming to assist users in discovering items of interest and aiding organizations in personalizing their offerings. Despite numerous advancements aimed at enhancing the efficiency and personalization of recommender systems, several persistent issues remain, including the cold start problem, data sparsity, and the limited interpretability of recommendations. Traditional recommender systems often struggle to handle these issues effectively due to their reliance on straightforward algorithms and lack of semantic understanding. This survey aims to provide a detailed review of recent research efforts focused on leveraging Knowledge Graphs and ontologies to enhance recommender systems. We present a fine-grained analysis of relevant studies, highlighting key methodologies, findings, and advancements in this field. Additionally, we offer insights into important datasets and tools that facilitate research and development in this domain. By consolidating this information, we aim to contribute to a better understanding of how semantically empowered techniques can transform recommender systems and pave the way for future innovations.