Predicting Consumer Behavior in E-Commerce Using Recommendation Systems
Year: 2024
Type: article
Abstract: As e-commerce platforms continue to expand, understanding consumer behavior has become crucial for enhancing customer satisfaction and driving business success. Recommendation systems play a pivotal role in predicting consumer preferences and delivering personalized product suggestions. This paper presents an extensive literature review on recommendation techniques, including collaborative filtering, content-based approaches, and hybrid models. Notable advancements, such as the use of deep learning, trust-based filtering, and context-aware models, are highlighted. Building on these foundations, we propose a novel model that integrates advanced machine learning algorithms with consumer behavior analysis to predict preferences more accurately. The expected results suggest that this model will improve the precision of recommendations, effectively addressing challenges like data sparsity and evolving user preferences and enhancing overall customer engagement in e-commerce environments
Source: Figshare
Author: Suryawanshi, Nikhil Sanjay
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6920907602
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