Recommendation System Building based on CNN and TF-IDF Approaches

Abstract

Online shopping has becoming increasingly popular. Customers remain engaged with shopping sites due to effective product recommendations. By analyzing users’ past actions and preferences, shopping platforms effectively identify customer interests and desires, enhancing their overall shopping experience. This paper presents a recommendation system that displays 5 items relevant to the product currently being viewed by the customer. The system processes images with an adapted ResNet-50, a deep learning image classification model in convolutional neural network (CNN) and use the acquired embedded vector to determine similarities with cosine functions. Additionally, the system employs term frequency-inverse document frequency (TF-IDF) method for text analysis in product descriptions, generating word embedding that assists recommendations. This blend of visual and textual analysis ensures that the suggestions closely match the item’s category. The system achieves 94.72% accuracy in subcategories’ classification, a 4% increase compared to using the CNN method alone, confirming its effectiveness in recommending relevant items.