Enhanced Customer Content Based Recommendation System

Abstract-Recommender systems employ various data mining techniques and algorithms to discern user preferences from a vast array of available items. Unlike static systems, recommender systems foster increased interaction to offer a more enriched experience. By analyzing past purchases, searches, and other users' behavior, these systems can autonomously identify recommendations for individual users. This technique leverages user history data, as well as other users' data, to predict preferred items and make personalized recommendations. This research paper focuses on the challenges faced by recommender systems, such as the cold start problem, data sparsity, scalability, and accuracy. Specifically, it delves into content-based filtering, which generates recommendations based on a user's behavior. Similar to collaborative filtering, content-based filtering relies on long-term user preference profiles that can be updated to enhance the performance