Open AccessMovie Recommended System by Using Collaborative Filtering
Author(s) -
Bheema Shireesha,
Navuluri Madhavilatha,
Chunduru Anilkumar
Publication year - 2019
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit19511
Subject(s) - recommender system , collaborative filtering , computer science , scalability , profit (economics) , big data , information retrieval , world wide web , data science , data mining , database , economics , microeconomics
Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.