SavvyShoper - Smart Grocery Recommender and Price Comparison System Using Machine Learning

The rapid growth of e-commerce has made consumer reviews, opinions, and shared experiences vital sources of information that reflect customer preferences. These reviews are invaluable for developing recommender systems, which help guide potential buyers towards preferred products. However, the complexity of extracting and utilizing textual data poses a significant challenge in formalizing a comprehensive mechanism for selecting and retrieving relevant opinions from the vast amount of information available. This paper introduces a novel recommender system based on consumer product reviews, proposing a prioritizing mechanism to enhance the selection and retrieval of opinions. The system's effectiveness is demonstrated through a case study involving the recommendation of digital cameras, showcasing how this method can effectively utilize consumer reviews. The proposed approach provides a significant improvement in the ability to analyze and leverage user-generated content in recommender systems. As e-commerce continues to expand, these systems have become essential tools for both businesses and consumers, simplifying the process of managing business transactions online and making informed purchasing decisions through tailored recommendations based on customer feedback.