APPLICATION OF COLLABORATIVE FILTERING ALGORITHM IN PERSONALIZATION OF PRODUCT RECOMMENDATIONS IN E-COMMERCE

Abstract

The number of people using and transacting in e-commerce has skyrocketed in recent years. To help people locate items that are a good fit for them, the recommendation system becomes crucial. This research suggests that e-commerce recommendation systems use Collaborative Filtering methods. Collaborative filtering is a technique that makes suggestions based on the tastes of users who are similar to the one making the suggestion. Rapid Application Development (RAD) is the research approach used, and it consists of four phases: planning, design, development, and cutover. Metrics like as Mean Absolute Error, Root Mean Squared Error, and F-Score are used to assess and evaluate systems. Incorporating Collaborative Filtering methods into e-commerce recommendation systems improved both suggestion accuracy and user happiness. But there's still work to be done to fine-tune the algorithm and make the system flexible enough to accommodate evolving user preferences and habits.