A recommender system is a software that filters down users' choices and provides them with the most suitable suggestions based on their requirements or preferences. The first recommender system was created in the 1970s at Duke University, and since then, they have become increasingly popular among big Internet companies such as Facebook, Amazon, Netflix, Google, YouTube, and Tripadvisor. Recommender systems aim to provide personalized recommendations by achieving four secondary goals: overcoming cold start issues, data sparsity, scalability, and diversity and novelty. These systems work with two kinds of data: user-item interactions and attribute information about users and items. There are various types of recommender systems, including collaborative filtering, content-based filtering, knowledge-based systems, and demographic systems. Hybrid models combining different approaches can also be effective. Recommender systems can be used for cross-selling and upselling, and their performance can be evaluated through A/B testing. The primary purpose of AI-based ecommerce recommendation systems is to help users choose the right products based on real data on previously liked or shown interest in products. As technology advances, recommender systems will become more sophisticated, allowing for real-time targeting of new user segments across multiple channels.