A Model for Movie Classification and a Genre-based Recommender System
Pegah Vahed, Azadeh Tabatabaei, Leila Taherkhani · 2024
Genre-based movie classification is a significant topic in text processing and machine learning. An automatic system that classifies movies by genre can help people find their top picks from an available variety. This research seeks to establish a model for genre-based movie classification utilizing machine learning algorithms and a movie dataset including details like movie titles, plot synopsis, actors, and genre information. Various methods such as logistic regression, decision tree, Naïve Bayes, and convolutional neural networks, combined with LSTM in Python, are used to train the movie classifier model. Using language models such as word2vec and TF-IDF, which transform words into numerical vectors, is one popular technique. This improves the accuracy of the model’s genre prediction. The proposed model achieves a high F1-Score of 0.84, which is comparable with the state-of-the-art algorithms that achieved F1-Scores of 0.82-0.92. Users can utilize this system to locate movies of their preferred genre by inputting the title or description and receiving recommendations. Using a web API, this system may also automatically suggest movies collaboratively.