摘要
信息过载是大数据环境下最严重的问题之一,推荐系统作为有效缓解该问题的方法,受到工业界和学术界越来越多的关注.如何充分利用丰富的用户反馈、社会化网络等信息进一步提高推荐系统的性能和用户满意度,成为大数据环境下推荐系统的主要任务.首先,对近几年大数据环境下的推荐系统进行了综述,对大数据和推荐系统进行了概述,对推荐系统在传统环境下和大数据环境下的区别进行了辨析;然后,根据层次化的框架对推荐系统关键技术、效用评价以及应用实践等进行了概括、比较和分析;最后,对大数据环境下推荐系统有待深入研究的难点和发展趋势进行了展望.
Information overload is one of most critical problems in big data, and recommendation systems which are powerful methods to solve this problem are coming under growing attention by industry and aca- demia. The main task of recommendation systems in big data is to improve the performance and accuracy along with user satisfaction utilizing user feedback, social network and other information. A survey of the recommendation systems in the big data is proposed, which includes the summarization of big data and recommendation systems, the differences between the recommendation systems in traditional environment and in big data, key techniques, evaluation and typical applications according to a hierarchical framework. Finally, the prospects for future development and suggestions for possible extensions are also discussed.
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2015年第2期1-15,共15页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(60872051)
北京市教育委员会共建项目
关键词
大数据
推荐系统
关键技术
big data
recommendation systems
key technology
