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International Journal of Engineering & Technology Sciences
IJETS 2024, 10 pages - Article ID:
Innovative Ai Solutions For Enhancing Knowledge Graph In Iran's Online Shopping Platforms: A Case Study Of Digikala.com
a Faculty Of Engineering And Technology, Shomal University, Amol, Iran
b School Of Business Administration,islamic Azad University,qaemshahr,mazandaran,iran
ABSTRACT
This review paper explores the integration of innovative AI solutions to enhance knowledge graphs within Iran’s online shopping platforms, with a particular focus on Digikala.com, the leading e-commerce platform in the country. Knowledge graphs, which organize data into interconnected entities and relationships, play a crucial role in improving user experiences through personalized recommendations, enhanced search functionalities, and efficient inventory management. However, implementing AI-driven knowledge graphs in the Iranian context presents unique challenges, including linguistic complexities, limited access to advanced technologies, and cultural nuances. This paper discusses various opportunities, such as the development of tailored natural language processing (NLP) models for Farsi, the use of graph neural networks (GNNs) to improve data relationships, and the establishment of automated machine learning (AutoML) pipelines to optimize model performance. It also highlights the importance of fostering a data-driven culture within Digikala to leverage insights from the knowledge graph effectively. By addressing the challenges and embracing innovative solutions, Digikala can enhance its knowledge graph capabilities, ultimately leading to improved customer satisfaction and competitive advantage in the rapidly growing Iranian e-commerce market. The insights from this case study may serve as a reference for other platforms in similar markets looking to implement AI-driven knowledge graphs successfully.