Content-Based Product Recommendation in E-Commerce

Abstract

Abstract—The recommendation system is a significant technique used in different E-commerce systems. As Ecommerce continues to flourish, more and more people are choosing to shop online, which has led to a vast selection of products available for purchase. To help consumers traverse through this immense array of products, the recommendation system is utilized to suggest items based on the user's preferences and past behavior. This technology assists users in finding fitting products that they may not have discovered on their own, making their shopping experience more efficient and enjoyable. This paper covers the topic of content-based recommendation systems, which are outlined to recommend items to users based on the elucidation of the item and the user's interests. In order to implement content-based product recommendation, this method relies on techniques such as TFIDF (Term Frequency Inverse Document Frequency) and Cosine Similarity, to dictate the importance of a product to a user's interests. By using these methods content-based recommendation systems can provide customized recommendations that agree with the user's distinctive bias and interests.