摘要
在线社交网络信息的主要作用是提高推荐系统的精度,但仅仅依靠传统的评分推荐系统(RS)是无法实现的.现在,为了更好地为用户在各活动中提供服务,许多在线社交软件支持一种称为"朋友圈"的新功能,该功能对"朋友"重新进行了定义.论文提出了一种基于朋友圈的推荐系统.该推荐系统旨在处理可用的评分数据并结合社交网络数据推断出特定类别领域的社会信任圈子.主要根据系统预测出的专业水平对圈内朋友划分不同的等级.通过对公开的数据进行验证实验,验证了本文所提出的基于朋友圈的推荐模型可以更好地利用用户的社会信任信息,从而有效地提高推荐系统的准确性.
Online social network information promises to increase recommendation accuracy beyond the capabilities of purely rating/feedback-driven recommender systems(RS).As to better serve users’ activities across different domains,many online social networks now support a new feature of“Friends Circles”,which refines the domain-oblivious“Friends”concept.RS should also benefit from domain-specific“Trust Circles”.This paper presents an effort to develop circle-based RS.We focus on inferring category-specific social trust circles from available rating data combined with social network data.We outline several variants of weighting friends within circles based on their inferred expertise levels.Through experiments on publicly available data,we demonstrate that the proposed circle-based recommendation models can better utilize user’s social trust information,resulting in increased recommendation accuracy.
作者
张舒
王成强
李强
李慧
Zhang Shu;Wang Chengqiang;LI Qiang;LI Hui(School of Business,Huaihai Institute of Technology,Lianyungang 222005,China;Department of Computer Science,Huaihai Institute of Technology,Lianyungang 222005,China)
出处
《南京师大学报（自然科学版）》
CAS
CSCD
北大核心
2018年第4期72-78,共7页
Journal of Nanjing Normal University(Natural Science Edition)
基金
国家自然科学基金(61403156
61403155)
连云港市科技计划项目(JC1608
CG1611)
淮海工学院自然基金项目(Z2017012
Z2015012)
淮海工学院教学改革项目(XJG2017-2-5)
教育部协同育人项目(201702134005
201701028110)
