Development of an Interactive Web-Based Hybrid E-Commerce Recommendation System Using Neural Collaborative Filtering and Content-Based Filtering

In an increasingly digital era, technological advancements have transformed the way people shop and interact with e-commerce platforms. E-commerce offers a wide range of products and services online, but consumers often feel overwhelmed by the sheer number of choices. The importance of efficient and relevant services for consumers is a significant consideration in developing e-commerce systems. One effective solution is implementing a recommendation system that helps consumers find products that match their needs and preferences. This study develops an e-commerce recommendation system using neural collaborative filtering and content-based filtering methods, integrated with an interactive dashboard to improve user experience. With a learning rate of 0.01 and a batch size of 32, the developed neural collaborative filtering model shows a low testing error of 0.0705 and precision, recall, and F-measure of 0.9583, 0.8251, and 0.8867, respectively. The content-based filtering method uses the Cosine Similarity technique, achieving an Average Precision of 0.804 and an accuracy of 80%. The interactive dashboard was successfully developed to support system evaluation and user engagement. This study provides benefits for consumers who can find products according to their needs more quickly and for e-commerce business owners who can reach consumers more efficiently.