Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In today's online retail environment, consumers are exposed to a vast number of products, which often makes selecting suitable items difficult. To mitigate this information overload, recommendation engines have emerged as vital infrastructure for e-commerce platforms. These systems synthesize diverse data points—ranging from historical purchase records to real-time behavioural patterns—to curate bespoke product selections for individual users. While traditional methodologies like content-based and collaborative filtering remain foundational, developers still struggle with persistent obstacles such as sparse datasets, system scalability, and the "cold-start" phenomenon for new users. This review provides a systematic analysis of current algorithmic advancements and evaluation frameworks within the field. By identifying existing research gaps and performance limitations, this study outlines a roadmap for the next generation of personalized shopping technologies.
"E-COMMERCE RECOMMENDATION SYSTEM ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.b86-b94, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603114.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator