Movie Recommended System by Using Collaborative Filtering

Authors(3) :-Bheema Shireesha, Navuluri Madhavilatha, Chunduru Anilkumar

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 itemís 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.

Authors and Affiliations

Bheema Shireesha
Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
Navuluri Madhavilatha
Guest Faculty, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India
Chunduru Anilkumar
Assistant Professor, Department of Computer Science and Engineering, Dr. APJ Abdul Kalam IIIT Ongole, Andhra Pradesh, India

Hadoop, Collaborative filtering, Machine Learning, HDFS, RSSI

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Publication Details

Published in : Volume 5 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 09-15
Manuscript Number : CSEIT19511
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Bheema Shireesha, Navuluri Madhavilatha, Chunduru Anilkumar, "Movie Recommended System by Using Collaborative Filtering", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 1, pp.09-15, January-February-2019. Available at doi : https://doi.org/10.32628/CSEIT19511
Journal URL : http://ijsrcseit.com/CSEIT19511

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