Using Trust of Social Ties for Recommendation

Abstract

SUMMARYNowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on coldstart users. key words: social network, trust-based, collaborative filtering, random walk

Sistema de recomendação baseado na relação de amizade entre usuários de redes sociais num cenário de cold-start.

“…Assim, o uso de informação de redes sociais pode ser usada para aprimorar a qualidade das recomendações e já são várias as iniciativas usando tais dados dentro do processo de recomendação (CHEN et al, 2016;Diaz-Agudo et al, 2018).…”

“…Vários trabalhos abordam recomendação de filmes (Christou et al, 2016;Moreno et al, 2016), sítios de interesse (Khalid et al, 2014;Yin et al, 2015), programas de TV (Zhang et al, 2013), noticias online (Lin et al, 2014) e grupos de estudo virtual (Salehi et al, 2013), dentre outros. Para diminuir a dispersão dos dados e mitigar o problema de cold-start, geralmente são exploradas diversas fontes para incrementar a informação sobre os usuários ou itens (Barjasteh et al, 2015;CHEN et al, 2016;Jain et al, 2015;Lian et al, 2016;Mohammadi and Andalib, 2017;Wang et al, 2016;Xiushan and Dongfeng, 2017;Zhang, Jia Dong et al, 2017).…”