Recommender system is an AI-based tool which suggests items to users based on their preferences, helping overcome information overload and improving user experience. This paper provides an introduction to recommender systems and their applications in variety of fields such as music streaming, networking sites, internet shopping, and digital media platforms. It highlights the benefits of personalized recommendations and identifies common challenges faced by recommender systems, including privacy concerns, cold start, data sparsity, and scalability issues. The paper proposes a hybrid model that combines content-based filtering (CBF) and collaborative filtering (CF) techniques for movie recommendations. The Movielens 1M dataset is used for evaluation, and the performance of the model is measured using the root mean squared error (RMSE). The results show that the hybrid recommender system outperforms both CBF and CF systems in terms of RMSE and accuracy, providing more accurate and personalized movie recommendations.

Recommender System; Hybrid Recommender System; Root Mean Square Error; Machine Learning.

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