Open AccessA Research on Collaborative Filtering Based Movie Recommendations: From Neighborhood to Deep Learning Based System
Author(s) -
Dayal Kumar Behera,
Madhabananda Das,
Subhra Swetanisha
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d4362.118419
Subject(s) - recommender system , collaborative filtering , computer science , information overload , popularity , domain (mathematical analysis) , deep learning , artificial intelligence , factor (programming language) , information retrieval , world wide web , psychology , social psychology , mathematical analysis , mathematics , programming language
Recommender System or Recommendation Engine gaining popularity as it can tackle information overload problem. Initially it was considered as a domain of Information Retrieval system and was limited to few applications. With the advancement of different state-of-the-art modeling approaches recommender system can be applicable to many application domains. Movie Recommender System (MRS) is widely explored domain and used by many streaming service providers like Netflix, Amazon Prime, YouTube and many more. This system makes use of users’ data to explore and recommends personally as per their taste. In this paper a detailed study on recently published article related to movie recommendation is carried out. Popular techniques for MRS are commonlycategorized into collaborative filtering, content-based and hybridmethod. Neighborhood-based, latent factor based, neural network based and deep learning based techniques have been continuously evolved with application to MRS. Recently proposed models have been reviewed and it is found that hybrid method performs better as compared to individual model.