An integrated recommendation approach based on influence and trust in social networks

Weimin Li, Zhengbo Ye, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In real human society, influence on each other is an important factor in a variety of social activities. It is obviously important for recommendation. However, the influence factor is rarely taken into account in traditional recommendation algorithms. In this study, we propose an integrated approach for recommendation by analyzing and mining social data and introducing a set of new measures for user influence and social trust. Our experimental results show that our proposed approach outperforms traditional recommendation in terms of accuracy and stability.

Original languageEnglish
Title of host publicationFuture Information Technology
PublisherSpringer Verlag
Pages83-89
Number of pages7
ISBN (Print)9783642550379
DOIs
Publication statusPublished - 2014 Jan 1
Event9th FTRA InternationalConference on Future Information Technology, FutureTech 2014 - Zhangjiajie, China
Duration: 2014 May 282014 May 31

Publication series

NameLecture Notes in Electrical Engineering
Volume309 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference9th FTRA InternationalConference on Future Information Technology, FutureTech 2014
CountryChina
CityZhangjiajie
Period14/5/2814/5/31

Keywords

  • influence
  • recommendation algorithms
  • similarity
  • social trust

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Li, W., Ye, Z., & Jin, Q. (2014). An integrated recommendation approach based on influence and trust in social networks. In Future Information Technology (pp. 83-89). (Lecture Notes in Electrical Engineering; Vol. 309 LNEE). Springer Verlag. https://doi.org/10.1007/978-3-642-55038-6_13