Movie Recommendation using Web Crawling
Abstract
In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends.
Cite
@article{arxiv.2412.10714,
title = {Movie Recommendation using Web Crawling},
author = {Pronit Raj and Chandrashekhar Kumar and Harshit Shekhar and Amit Kumar and Kritibas Paul and Debasish Jana},
journal= {arXiv preprint arXiv:2412.10714},
year = {2024}
}
Comments
12 pages, 3 figures, Accepted and to be published in Proceedings of 2025 International Conference on Applied Algorithms (ICAA), Kolkata, India, Dec 8-10, 2025