This study aims to develop a WebCF-Exp recommendation system that adds explanation functionality to improve the recommendation quality and trustworthiness of existing collaborative filtering-based product recommendation systems, and to evaluate the performance of this system. Existing collaborative filtering-based product recommendation systems, despite their excellent recommendation performance, lack the ability to explain the reasons for recommendations to users, which easily leads to insufficient customer trust in recommended products. This research provides various forms of explanation functionality by presenting customers with the reasons for recommendations in the existing collaborative filtering-based product recommendation system that applies web mining and product hierarchies, thereby helping customers to trust the system. Furthermore, the recommendation system WebCF-Exp incorporating these explanation features was developed, and an internet shopping mall was implemented for experiments to verify its effectiveness. Finally, through an online survey targeting customers who used the simulated internet shopping mall, the study analyzed which of the 20 different explanation interfaces was most appropriate for customers to understand, and to what extent the addition of explanation functionality to recommended products could assist users in their purchase decision-making.