大数据环境下的移动社交网络推荐算法

Big Data Environment of Mobile Social Network Recommendation Algorithm

社交网络每天都会产生半结构、结构化及非结构化的大量数据,数据的增长速度也远远超出了硬件需求的摩尔定律。目前,社交网络中还存在恶意评论、刷网站关注度等现象,都严重影响了大数据的分析和处理。为了能够有效提高大数据的处理效率及网站推荐的精准度,本文提出了一个个性化的推荐模型,并且分析了此模型的推荐算法,以此能够提高用户使用的满意度,实现准确及高质量的个性化推荐。

social network produces half- structured and unstructured data structure everyday, data growth is fat＂ beyond the hardware requirements of Moorcg law. At present, the social networking phenomenon such as malicious comments, brash website visibility, they have seriously ＂affected the big data analysis and processing. In order to be able to effectively improve the process- ing efficiency of large data and website recommendation accuracy, this paper proposes a personalized recommendation model, and analyzes the recommendation algorithm of this model, in order to improve the satisfaction of users and achieve accurate and high quality personalized recommendation.