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.
