Recommender Systems: A Comprehensive Review of Models, Techniques, and Emerging Trends

Recommender Systems: A Comprehensive Review of Models, Techniques, and Emerging Trends

Ms. Nansi Jain*, Ajeet Singh Yadav, Kanishk Sharma, Shivansh Dubey, Shreyash Shandilya

IPEC, Dept. of CSE(DS), Ghaziabad, Uttar Pradesh

Section I: Abstract

   Recommendation systems' main goal is to provide a consumer with relevant objects based solely on historical data. A movie that has received a high rating from a customer who has also seen the film you are currently viewing may appear in the suggestions. It's likely that almost everyone will enjoy the movies with the highest total scores. CineMatch is the name of the algorithm that performs all of these functions. Regarding individual users, it also gains insight from their behavior to enhance the likelihood that they will find a film engaging. Here, we need to apply cutting-edge collaborative filtering approaches to boost our CineMatch algorithm by 10%. Keywords: Sparse matrix, Similarity matrix, Movies, Ratings, Machine learning models.