Collaborative Filtering Approach for Improved Recommender System by VADER and TextBlob

This paper presents a grocery recommendation system using collaborative filtering to enhance user shopping experience and increase sales. The proposed method leverages both user-based and item-based collaborative filtering techniques to analyze purchase history and identify patterns among users with similar shopping behaviors. By comparing a user's preferences with those of similar users, the system generates personalized product recommendations. This approach aims to address the challenge of providing relevant and diverse suggestions in grocery retail, where user preferences are dynamic and varied. Experimental findings reveal that the proposed system achieves high accuracy in predicting user preferences, presenting a practical and effective solution for recommendation systems in the grocery domain.