• Monica S
• Krishna Gudi
• Aruna P
• Dhruv jyoti Shukla
• Dhanalakshmi B

In digital era, we have wide-range of resources such as items, music, books, movies and so on, finding the data based on user's interest becomes unerring, thus recommender systems will play a major role. A recommendation engine is all about suggesting a similar type of content or items by predicting user preferences. Eg: In case of OTT platforms the movies to watch, in the case of e-commerce sites the items to purchase will be suggested using recommender systems. This can be done by using Machine learning (ML) algorithms such as Content-based filtering (CBF), Collaborative filtering (CF), and hybrid filtering approach (HF). CF is classified into model-based approach and memory based approach. HF is a technique which is a combination of both content based and model based CF approach. XG Boost is used for improving the performance of recommendation engine

learning (ML), user choice's, Hybrid Filtering (HF), Collaborating filtering (CF), Recommendation, XG Boost

"Movie Recommendation System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.f149-f152, April-2022, Available :http://www.jetir.org/papers/JETIR2204519.pdf

"Movie Recommendation System Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppf149-f152, April-2022, Available at : http://www.jetir.org/papers/JETIR2204519.pdf