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Graph based Recommendation System in Social Networks

by Honey Jindal, Anjali
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 2
Year of Publication: 2015
Authors: Honey Jindal, Anjali
10.5120/19801-1583

Honey Jindal, Anjali . Graph based Recommendation System in Social Networks. International Journal of Computer Applications. 113, 2 ( March 2015), 36-40. DOI=10.5120/19801-1583

@article{ 10.5120/19801-1583,
author = { Honey Jindal, Anjali },
title = { Graph based Recommendation System in Social Networks },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number2/19801-1583/ },
doi = { 10.5120/19801-1583 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:57.191635+05:30
%A Honey Jindal
%A Anjali
%T Graph based Recommendation System in Social Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 2
%P 36-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Media content recommendation is a popular trend now days. Twitter, Facebook, and Google+ are very popular in the world. The growth of social networks has made recommendation systems one of the intensively studied research area in the last decades. Recommendation systems can be based on content filtering, collaborative filtering or both. In this paper, we propose a novel approach for media content recommendation based on collaborative filtering. Firstly the user-user social network is created using most prominent neighbor set of each user by utilizing their preference information. Then these users are clustered using their neighbor sets and the user with maximum neighbor count is chosen as cluster head. When new user searches for its cluster its similarity is calculated with all the cluster heads. The user gets recommendation based on the average ratings of his cluster members. The proposed approach is tested on the users of Movielens Dataset. The proposed approach gives a hit ratio of 89. 33%, Mean Absolute Error as 0. 4756 and Root Mean Square Error as 0. 7671 on Movielens dataset.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Recommendation social networks content filtering collaborative filtering clustering preferences neighbor set.

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