Paper Info
Title
A semantic and social-based collaborative recommendation of friends in social networks.
Abstract
The development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user-based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social-behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user-based CF algorithm and solves the sparsity and cold start problems.
Year
DOI
Venue
2020
10.1002/spe.2828
SOFTWARE-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
collaborative filtering,competency,credibility,friends' recommendation,friendship,hybrid filtering,semantic filtering,social filtering,social recommendation,trust,Yelp social network
Journal
50.0
Issue
ISSN
Citations 
8.0
0038-0644
1
PageRank 
References 
Authors
0.35
0
1
Name
Order
Citations
PageRank
Lamia Berkani1127.44