Collaborative Filtering: A Recommender System

Collaborative Filtering: A Recommender System

Shreya Gawali *1, Prof. Mrs. Netraja Mulay *2

*1 PG Student, Dept. of MCA, PES Modern

College Of Engineering, Pune Maharashtra, India

*2 Asst Professor, Dept. of MCA, Modern

College Of Engineering, Pune Maharashtra, India

Abstract:
This paper provides an in-depth exploration of collaborative filtering as a fundamental element in recommendation systems, Essential for improving customer happiness and bolstering company growth. It highlights the method's effectiveness in delivering tailored recommendations by analyzing users' past actions and preferences. Collaborative filtering doesn't solely enhance the user experience; it also nurtures user engagement and commitment. Additionally, the paper discusses how collaborative filtering addresses ethical concerns by reducing biases in algorithms and promoting fairness and diversity in recommendations. It emphasizes the extensive impact of collaborative filtering across various domains such as e- commerce sites, streaming of video, and music Apps, underscoring its efficacy in enhancing user interaction while upholding ethical standards in data practices.be an efficacious method that not only boosts user interaction but also maintains ethical standards in data practices, benefiting both businesses and their customers.

Keywords:
Recommender system, collaborative filtering, model-based recommendation, user-item matrix