Research Article | Open Access
MOVIE RECOMMENDATION SYSTEM
Mr M SUDHAKAR, HARSHITH REDDY PASHAM, ELDI SRINIVAS PHANI KUMAR, PALLAPU SANDHYA, VADDEMAN GAYATHRI SOUMYA
Pages: 739-749
Abstract
The purpose of this project is to develop a movie recommendation system using Python and
machine learning algorithms, specifically cosine similarity. The system aims to provide
personalized movie recommendations to users based on their preferences and similarities to other
users. The cosine similarity algorithm will be used to measure the similarity between movies and
users, allowing for effective recommendation generation. The project involves data collection and
pre-processing, where a dataset of movie ratings and user information will be gathered. Feature
extraction techniques will then be applied to extract relevant information from the dataset,
such as genre, director, and actors. The cosine similarity algorithm will be implemented to compute
the similarity scores between movies and users based on their shared features. Evaluation metrics
will be employed to assess the performance of the recommendation system, such as precision,
recall, and accuracy. The experimental setup will involve splitting the dataset into training and
testing sets, ensuring the robustness of the system. Results and analysis will be presented to
showcase the effectiveness of the system in providing accurate and personalized
recommendations. In conclusion, this project aims to develop a movie recommendation system
using Python and the cosine similarity algorithm, providing users with personalized movie
suggestions based on their preferences. The implementation of this system has the potential to
enhance the movie-watching experience and facilitate movie discovery for users. Future work may
involve incorporating additional machine learning algorithms and enhancing the system's
scalability.
Keywords
Machine Learning, Content-Based Filtering, Cosine Similarity.