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Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System

by Poonam B. Thorat, R. M. Goudar, Sunita Barve
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 4
Year of Publication: 2015
Authors: Poonam B. Thorat, R. M. Goudar, Sunita Barve
10.5120/19308-0760

Poonam B. Thorat, R. M. Goudar, Sunita Barve . Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications. 110, 4 ( January 2015), 31-36. DOI=10.5120/19308-0760

@article{ 10.5120/19308-0760,
author = { Poonam B. Thorat, R. M. Goudar, Sunita Barve },
title = { Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 4 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number4/19308-0760/ },
doi = { 10.5120/19308-0760 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:46:40.526537+05:30
%A Poonam B. Thorat
%A R. M. Goudar
%A Sunita Barve
%T Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 4
%P 31-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems or recommendation systems are a subset of information filtering system that used to anticipate the 'evaluation' or 'preference' that user would feed to an item. In recent years E-commerce applications are widely using Recommender system. Generally the most popular E-commerce sites are probably music, news, books, research articles, and products. Recommender systems are also available for business experts, jokes, restaurants, financial services, life insurance and twitter followers. Recommender systems have formulated in parallel with the web. Initially Recommender systems were based on demographic, content-based filtering and collaborative filtering. Currently, these systems are incorporating social information for enhancing a quality of recommendation process. For betterment of recommendation process in the future, Recommender systems will use personal, implicit and local information from the Internet. This paper provides an overview of recommender systems that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

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Index Terms

Computer Science
Information Sciences

Keywords

Content-Based filtering collaborative-filtering Hybrid Recommendation System Data sparsity

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