An Empirical Approach of Movie Recommendation System Using Machine Learning (NLP)

Recommendation systems play a significant role in helping users make informed automated decisions. These systems facilitate users in discovering valuable information from a vast pool of available data. In the context of movie recommendation systems, recommendations are generated based on user similarities (Collaborative Filtering) or by taking into account an individual user’s interests (Content-Based Filtering). To address the limitations of both collaborative and content-based filtering, a hybrid approach is often employed to create a more effective recommendation system. Additionally, various similarity measures are used to determine user similarities for recommendations. This paper provides an overview of state-of-the-art techniques in Content-Based Filtering, Collaborative Filtering, Hybrid Approaches, and Deep Learning-Based Methods for movie recommendations. Furthermore, we discuss different similarity measures [1] [2].Various organizations, such as Instagram, LinkedIn, Spotify, Disney + Hotstar, and Amazon, utilize recommendation systems to enhance their profits and cater to their customers’ needs. This paper primarily focuses on a concise review of diverse techniques and their methods for movie recommendations, thereby encouraging further research in the domain of recommendation systems.