| dc.description.abstract | E-commerce marketplaces heavily rely on advanced product search and recommen dation technologies to enhance user experience, improve customer satisfaction, and
drive sales. However, when businesses transition to e-commerce marketplaces, they
face unique challenges in product searching and recommendation systems compared to
traditional physical stores. This review investigates the effectiveness of various search
and recommendation techniques in addressing these challenges, specifically focusing on
issues like diverse product catalogues, complex product attributes and compatibility of
selected products or items related to searching functionality, and issues like data sparsity,
cold start, and limited user history related to product recommendations. The study
aims to analyse how different techniques and methods, including Natural Language
Processing (NLP), machine learning, data analysis, collaborative filtering, content-based
filtering, user queries, search algorithms, catalogue navigation, information retrieval,
and other techniques (e.g., transformer models, Siamese networks, Word2vec) are used
in product searching and recommendation. This study outlines how these technologies
and methods contribute to effectiveness, customer confidence, and personalization. The
review findings highlight how integrating various search methods and utilizing hybrid
recommendation strategies for businesses can significantly improve user experience,
enhance customer satisfaction, and drive higher conversion rates. Including Q&A
functionalities further enriches the user experience and provides valuable insights for
both customers and businesses. These findings have significant implications for the
design and development of future e-commerce platforms, guiding the creation of more
effective and user-centric systems and enhancing the overall shopping experience for
online consumers. | en_US |
