• Lahari Muppalla
• Ch.Hima bhanu
• N.Bhavya Sai Sree
• B.Vijaya Babu

Recommendation systems are a useful tool that helps users navigate vast amounts of information. The Movie Recommendation System uses either a specific user's activity or user similarities to make suggestions. Regarding the Movie Recommendation System, suggestions are made either by taking into account a specific user's activity that he wishes to interact with or by comparing user similarities. By integrating content-based and collaborative filtering, a recommendation system can be improved and overcome some of its shortcomings. Additionally, a range of similarity metrics are employed to determine user similarity for recommendation purposes. The most recent techniques are content-based filtering, collaborative filtering, hybrid filtering Association Rule Approach, and Deep Learning Methods for movie recommendations.

Movie streaming services; recommender systems; content-based filtering; collaborative filtering

"Enhancing Movie Recommendations: Comparing Collaborative and Content-Based Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.K473-K476, June-2024, Available :http://www.jetir.org/papers/JETIR2406A58.pdf

"Enhancing Movie Recommendations: Comparing Collaborative and Content-Based Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppK473-K476, June-2024, Available at : http://www.jetir.org/papers/JETIR2406A58.pdf