Applying Collaborative Filtering in E-Marketing Systems

Abstract

Collaborative filtering is very important in e-marketing systems since it enables users to search through the large product lists and become better buyers. This paper will look into the application of machine learning algorithms for instance K-Nearest Neighbors (KNN) and Decision Tree Classifier in formulating a collaborative filtering for supermarket products like, food, dairy, canned goods, juices and detergents. Three data files were employed; a supermarket products file of five Categories each having ten products, a supermarket manager’s offers file of the same Categories with each set with four products, and a file of past user’s purchases. The methodology employed involves using a user product matrix to capture purchasing behavior, using KNN algorithms to predict user preferences using the distance of Euclidean, also, a decision tree to set up the decision-making rules for products recommendations implemented. The number of the previous user’s purchases is shown in a histogram. The paper compares the KNN algorithm with the decision tree algorithm on recommendation accuracy, precision, recall, F1-score and execution time. The KNN algorithm yielded the best accuracy (0.9651), precision (0.8233), recall (0.8430), F1 score (0.8293) and execution time (0.896). The decision tree algorithm yielded lower accuracy (0.8392), precision (0.2094), recall (0.2111), F1 score (0.2072), and execution time (3.686).