A Recommendation Model Based on User Behaviors on Commercial Websites Using TF-IDF, KMeans, and Apriori Algorithms

Abstract

In recent years, recommendation systems have been widely introduced in various domains. The three main types of recommendation system are content-based, collaborative and hybrid filtering. Nowadays, much research applies machine learning techniques to construct recommendation models. This research also implemented a recommendation model using three machine learning techniques: TF-IDF, KMeans, and Apriori algorithms. TF-IDF was applied to form word vectorization from webpage headings. KMeans was utilized for clustering webpage headings while the Apriori algorithm was employed to find the association of webpage clusters. The elbow method was utilized to obtain the optimal number of clusters. KNN, Decision Tree, and Multi-Layer Perceptron were employed to evaluate the prediction accuracy. The dataset analyzed in the research was collected from a specific commercial website. User behaviors on the website were considered as the dataset in the research. The recommendation lists were retrieved from webpages in the same cluster and associated clusters. The prediction accuracy of the proposed model was approximately 88.62%.