Premium
A semantic and social‐based collaborative recommendation of friends in social networks
Author(s) -
Berkani Lamia
Publication year - 2020
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2828
Subject(s) - novelty , computer science , credibility , social network (sociolinguistics) , friendship , recommender system , collaborative filtering , focus (optics) , dimension (graph theory) , social media , similarity (geometry) , semantic similarity , cold start (automotive) , world wide web , information retrieval , artificial intelligence , psychology , social psychology , physics , mathematics , optics , political science , pure mathematics , law , aerospace engineering , engineering , image (mathematics)
Summary 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.