摘要
[目的/意义]当前,好友推荐被广泛地应用于社交网络平台,高质量的好友推荐在满足用户交友需求、帮助用户获取更多信息资源的同时,还提高了用户与社交网站的粘性。[方法/过程]通过对相关文献的分析研究,界定了好友推荐的概念,梳理了社交网络中好友推荐的过程,归纳了好友推荐方法,总结了现有相关研究存在的不足及研究展望。[结果/结论]研究发现,社交网络中的好友推荐依据用户在社交网络平台上留下的记录,帮助用户找到他们感兴趣的其他用户。现有的好友推荐存在冷启动、数据稀疏、动态变化、信息茧房、用户隐私等问题,未来好友推荐可以从推荐的多维性、提供好友推荐结果的解释、设置用户反馈机制以及改进推荐系统的可拓展性等方面进行研究。
[Purpose/Significance]At present,friend recommendation is widely used in social network platforms.High quality friend recommendation can not only meet users needs for making friends and help users obtain more information resources,but also improve the stickiness between users and social networks.[Method/Process]Through combing,summarizing and summarizing the relevant literature,this paper defined the concept of friend recommendation,combed the process of friend recommendation in social networks,summarized the methods of friend recommendation,and summarized the shortcomings of existing relevant research and the focus of future research.[Result/Conclusion]The research found that friend recommendation in social networks helps users find other users they are interested in based on the records left by users on social network platforms.The existing friend recommendation research has problems such as cold start,data sparsity,dynamic change information cocoon room and privacy problems.In the future,friend recommendation can be studied from the aspects of multidimensional recommendation,providing explanations for results of friend recommendation,setting user feedback mechanism and improving the scalability of the recommendation system.
作者
杨瑞仙
刘莉莉
楚晨
于政杰
Yang Ruixian;Liu Lili;Chu Chen;Yu Zhengjie(School of Information Management,Zhengzhou University,Zhengzhou 450001,China;Zhengzhou Research Center of Data Science,Zhengzhou 450001,China;Central Big Data Innovation Center,China Academy of Information and Communications Technology,Zhengzhou 450001,China)
出处
《现代情报》
2023年第4期28-38,共11页
Journal of Modern Information
基金
河南省高等学校哲学社会科学基础研究重大项目“融入多用户属性的网络知识社区核心用户识别与推荐研究”(项目编号:2023-JCZD-27)。
关键词
社交网络
好友推荐
基于内容的好友推荐
基于社交关系的好友推荐
混合好友推荐
social network
friend recommendation
content-based friend recommendation
social relationship-based friend recommendation
hybrid friend recommendation
