An Item Based Collaborative Filtering for Similar Movie Search

V. Arulalan, Dhananjay Kumar () and V. Premanand
Additional contact information
V. Arulalan: Madras Institute of Technology, Anna University, Department of Information Technology
Dhananjay Kumar: Madras Institute of Technology, Anna University, Department of Information Technology
V. Premanand: Madras Institute of Technology, Anna University, Department of Information Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 949-955 from Springer

Abstract: Abstract A movie recommendation is vital in our social life because of its quality in giving improved excitement. Such a framework can recommend an arrangement of movies to users in light of their advantage, or the popularities of the movies. Despite the fact that, an arrangement of movie recommendation frameworks has been proposed, the vast majority of these either can’t prescribe a movie to the existing users productively or to another user by any methods. This paper proposes a movie recommendation framework that can extract from data and recommend similar movies to users, based on users input using Item based Collaborative Filtering. First, user item rating matrix is examined to identify relationships among various items, then finally similar movies were recommended based on user’s input. A part of this recommender system is execute using Apache Pig and Hadoop Distributed File System is used as data storage.

Keywords: Recommender; Item; Collaborative filtering; Rating matrix; Movies; Cosine similarity (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_96

Ordering information: This item can be ordered from
http://www.springer.com/9783030418625

DOI: 10.1007/978-3-030-41862-5_96

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().