基于社会媒体挖掘的推荐系统

随着互联网的出现,大数据时代迅速发展,用户在上网的过程中可以满足对各类信息检索的需要。但随着网络技术不断发展,信息数据量日益庞大,信息超载的现象随之而来。推荐系统通过统计分析用户的历史浏览记录挖掘用户在不同情况下的喜好,从数据库中检索与其匹配的内容,将相关内容推荐给用户,这是减少信息超载现象出现的高效办法。本文介绍了推荐系统后运用的各类推荐算法,将算法的原理及优缺点进行了相关的阐述和解析。

with the emergence of the Internet and the rapid development of big data era,users can meet the needs of various information retrieval in the process of surfing the Internet.However,with the continuous development of network technology,the amount of information data is increasing,and the phenomenon of information overload follows.Recommendation system through statistical analysis of users'historical browsing records,mining users'preferences in different situations,retrieving the matching content from the database,and recommending the relevant content to users,which is an efficient way to reduce the phenomenon of information overload.This paper introduces the various recommendation algorithms used after the recommendation system,and expounds and analyzes the principles,advantages and disadvantages of the algorithms.