Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ramachandran, Siddharth; Maria Deepti, T.T.; Vinodha, D.; Jenefa, J.; Mary Anita, E.A.; Lapina, Maria Title A name given to the resource Content-Based Product Recommendation SystemsReview Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1277 LNNS;pp.489-501 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2700-4_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2700-4_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011265713?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011265713?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ramachandran S., CHRIST (Deemed to be University), Bangalore, India; Maria Deepti T.T., CHRIST (Deemed to be University), Bangalore, India; Vinodha D., CHRIST (Deemed to be University), Bangalore, India; Jenefa J., CHRIST (Deemed to be University), Bangalore, India; Mary Anita E.A., CHRIST (Deemed to be University), Bangalore, India; Lapina M., North-Caucasus Federal University, Stavropol, Russian Federation Description An account of the resource Content-based recommendation systems have become essential for improving user experiences in e-commerce and various digital platforms. This review paper examines the recent advancements in content-based recommendation systems, focusing on machine learning techniques and models used to personalise user interactions. The paper also explores the role of deep learning and hybrid approaches in increasing the accuracy and relevance of recommendations. Despite significant progress, the product recommendation systems face challenges such as capturing complex user preferences, ensuring scalability, addressing the cold start problem, and improving explainability which remains crucial and requires further research. This paper offers a comprehensive overview of current methodologies, identifies existing limitations, and suggests future directions to optimise content-based recommendation systems to provide more effective and reliable recommendations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Content-based recommendation system; Deep learning; Natural language processing Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962699-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online