Movie Recommendation using Web Crawling

TL;DR

This paper presents a hybrid movie recommendation system that combines static datasets with real-time web crawling to improve personalization and relevance of suggestions.

Contribution

It introduces an integrated approach using web scraping, APIs, and hybrid filtering techniques to enhance movie recommendations with real-time data.

Findings

Real-time data integration improves recommendation relevance

Hybrid filtering outperforms single-method approaches

Dynamic data boosts user satisfaction

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.