Item-Based Collaborative Filtering for Personalized Laptop Recommendations

The laptop has grown to be one of the most important and widely used products in our everyday lives. However, given the variety of features and brand names available, it is safe to assume that many individuals have had difficulty selecting the personal laptop that best suits their demands. This study presents a laptop recommendation system that uses an item-based collaborative filtering algorithm to provide personalized recommendations to users. The study begins with a background study on recommendation systems and their importance in e-commerce business. The evaluation of the system's accuracy was conducted using the Mean Absolute Error (MAE) which the accuracy obtained is 89.37% and by using Root Mean Squared Error (RMSE) metrics with accuracy 87.55<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>• The results demonstrate that item-based collaborative filtering can improve the precision and customization of laptop recommendations and emphasizes the effectiveness in improving user satisfaction and increasing sales for e-commerce websites and online marketplaces.