[1]黄立威,李德毅.社交媒体中的信息推荐[J].智能系统学报,2012,7(01):1-8.
 HUANG Liwei,LI Deyi.A review of information recommendation in social media[J].CAAI Transactions on Intelligent Systems,2012,7(01):1-8.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年01期
页码:
1-8
栏目:
出版日期:
2012-02-25

文章信息/Info

Title:
A review of information recommendation in social media
文章编号:
1673-4785(2012)01-0001-08
作者:
黄立威1李德毅2
1.解放军理工大学 指挥自动化学院,江苏 南京 210007;
2.中国电子系统工程研究所,北京 100141
Author(s):
HUANG Liwei1 LI Deyi2
1. Institute of Automatic Commanding, PLA University of Science and Technology, Nanjing 210007, China;
2. Institute of Electronic System Equipment Engineering, Beijing 100141, China
关键词:
信息推荐信息过载推荐系统社交媒体
Keywords:
information recommendation information overload recommendation systems social media
分类号:
TP391
文献标志码:
A
摘要:
近年来社交媒体越来越流行,可以从中获得大量丰富多彩的信息的同时,也带来了严重的“信息过载”问题.推荐系统作为缓解信息过载最有效的方法之一,在社交媒体中的作用日趋重要.区别于传统的推荐方法,社交媒体中包含大量的用户产生内容,因此在社交媒体中,通过结合传统的个性化的推荐方法,集成各类新的数据、元数据和清晰的用户关系,产生了各种新的推荐技术.总结了社交推荐系统中的几个关键研究领域,包括基于社会化标注的推荐、组推荐和基于信任的推荐,之后介绍了在信息推荐中考虑时间因素时的情况,最后对社交媒体中信息推荐有待深入研究的难点和发展趋势进行了展望.
Abstract:
Social media has become tremendously popular in recent years, and much rich information can be derived from it. However, the massive amount results in a serious “information overload” problem. As one of the most effective methods to ease the “information overload” problem, recommender systems play an important role in social media. Social media contains a large amount of usergenerated content. Through the aggregation of all types of new data, metadata, and clear relationships between users and by combining the traditional method of personalized recommendations, a variety of new technologies emerge in recommender systems. This paper summarizes several key research areas in social recommender systems, including recommendations based on social tagging and group recommendations, as well as the recommendations based on trust. It then introduces several temporal aspects that affect social recommender systems, and finally proposes that the research difficulty be tackled while laying out the expectations for future development trends in the information recommendation system in social media. 

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备注/Memo

备注/Memo:
收稿日期: 2012-01-10.
网络出版时间: 2012-02-18.
基金项目:国家自然科学基金资助项目(61035004).
通信作者:黄立威.         E-mail:huangliwei.1985@gmail.com.
作者简介:
黄立威,男,1985年生,博士研究生,主要研究方向为社会网络分析、推荐系统
李德毅,男,1944年生,研究员,博士生导师,中国工程院院士,国际欧亚科学院院士,国家和全军信息化专家咨询委员会委员,中国人工智能学会理事长,中国电子学会副理事长,中国电子学会云计算专家委员会主任委员.主要研究方向为计算机工程、人工智能和指挥自动化.先后获得国家科技进步奖等奖项17项、国家发明专利7项,曾被授予国家首届优秀回国留学人员、国家有突出贡献的中青年专家,2005年获得何梁何利奖基金科学与技术进步奖,2006年获得中国人民解放军专业技术重大贡献奖.发表学术论文百余篇,出版专著5部、英文专著3部,主编技术丛书7种.
更新日期/Last Update: 2012-05-07