The recommender systems are typically made to ease the information search over the online systems so that the users find a more convenient way to connect to their preferences. Since various industries have moved from an age of little available data to the era of big data, the junk information available is so much that it can delay the decision-making process. They are also applied in job search platforms where the website suggests to a candidate the best possible positions fit for the skills. The major application is in e-commerce websites where they suggest to the users the products or services based on the information available such as past search, age, gender, and other preferences. The development of recommender systems depends on the field of application. A combination of various factors is used to assess the correlations in patterns and user characteristics to determine the best product suggestions for the customers. Recommender systems are designed to ease product or service searches based on the least information available about the features. The suggestions from the recommender systems help the system users find what is most suitable for them. Recommender systems work by assessing the available information about the likely patterns of the users and making suggestions from the information available. Perhaps the most widely exploited tool among data mining methods is recommender systems. Data mining methods can aid in obtaining and processing the relevant data and deal with the issue of information overload. Information overload may be defined as the state of being overwhelmed by the sheer volume of data presented to an average human for processing and decision making. However, the enormous data have also led to the problem of information overload. Digitalization of day-to-day experiences has led to the big data era. Modern technology has revolutionized the volume, variety, and velocity at which data are generated. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. This study conducts a systematic literature review on movie recommender systems. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. Movie recommender systems are meant to give suggestions to the users based on the features they love the most.
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