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This chapter deals with different concepts and challenges of recommendation systems, and how artificial intelligence and machine learning can be used for them. The chapter mainly focuses on the concepts and techniques used by the recommendation system for better suggestion. It also contains the challenges and applications or recommendation system. commerce, the recommendation machine has been widely used. In this paper, the electronic commerce recommendation system has a similar look at and makes a specialty of the collaborative filtering algorithm in the utility of personalized film recommendation system [7]. Generally, Recommendation systems work in two basic ways: Content-based and Collaborating Filtering. Content-based In the Content-based methods, the basis is the analysis of the content and characteristics of each item with the user's characteristics and information.For example, the system first examines the features of the items. or diversity of recommendations, can be prioritized. By combining more than one recommender system into a larger system, however, many of the weaknesses of applying machine learning algorithms in these systems has become one of the main topics due to their potential to improve such systems (Portugal, et al., 2015). Since there are a variety The aim is to reduce the human effort by suggesting movies based on the user's interests by introducing a model combining both content-based and collaborative approach. Nowadays, the recommendation system has made finding the things easy that we need. Movie recommendation systems aim at helping movie enthusiasts by suggesting what movie to watch without having to go through the long process Recommendation systems are generally classified into Content Based Filteringand Collaborative based Filtering. Both these techniques are unique in themselves and are based on the user-product recommender Systems have a promising accuracy, approaches used by them require many previous users to first rate items with respect to criteria. This paper presents a rating Criteria Recommendation System for food Recommendations to choose the best suited hotel in a city according to a users' preference and other user's ratings. The presented recommender system generates recommendations using various types of knowledge and data about users from the movie dataset. The user can then browse the recommendations easily and find a movie of their choice. Key Words: Recommendation systems, collaborative filtering, dataset, machine learning, user based recommendations 1. There are two methods to construct a recommendation system. 1. Content-based recommendation Uses attributes of items/users Recommend items similar to the ones liked by the user in the past 2. Collaborative filtering Recommend items liked by similar users Enable exploration of diverse content Content-Based Recommendation Hybrid Recommendation System - Intuition - Advantages - Disadvantages - Example - Implementation; Concluding Remarks; Resources; What is a Recommendation System. Recommendation engines are a subclass of machine learning which generally deal