Movie Recommendation using Web Crawling

• URL: http://arxiv.org/abs/2412.10714v1
• Date: Sat, 14 Dec 2024 06:56:46 GMT
• Title: Movie Recommendation using Web Crawling
• Authors: Pronit Raj, Chandrashekhar Kumar, Harshit Shekhar, Amit Kumar, Kritibas Paul, Debasish Jana,
• Abstract summary: 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.
• Score: 1.821917087370735
• License:
• 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.

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