Movie Recommendation System Based on User Ratings and Critique

Recommendation systems are information filtering techniques and tools that give valuable and meaningful suggestions to users about items in which they may be interested by considering various attributes. In this research presents a novel method to enhancing content-based movie recommendations by leveraging Natural Language Processing (NLP) techniques. The model utilizes a Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and the model extracts relevant topics from critics' consensus in movie critique. The topics are then employed to compute cosine similarity, which creates links between movies according to thematic content. User-friendly recommendations are ensured by converting audience ratings to a 0–5 scale. Latent Dirichlet Allocation (LDA) is used to categorize movies into distinct topics, which further optimizes the system. The model shows how NLP and machine learning can be combined to for enhanced user experiences on entertainment platforms.