摘要
数据挖掘作为一个交叉了统计学、数据库、机器学习等多个学科的新学科，近几年得到了广泛的重视，而SNS（社交网站）虽然有了庞大的用户基础，但仍需要更加个性化的开发，因此该文通过数据挖掘的分析方法，借助SNS庞大的信息来源，实现和分析了一种适用于SNS中的个性化推荐算法。该算法基于协同过滤技术，经过采集数据，计算用户的相似度，寻找最邻近用户，预测评分，并根据最终的近邻相似度和预测评分形成推荐。
As a new cross-disciplinary that intersects Statistics, Data Base, Machine Learning, data mining has got extensive attention in recent years. Though the SNS （Social Network Site） has a strong user base, development on personalized service is highly demanded. Therefore, a personalized recommendation algorithm appropriate for SNS, by means of data mining and vast information sources from SNS, is fulfilled and analyzed in this article. By using collaborative faltering, this algorithm is able to calculate the similarity, search for the nearest neighbor, predict the grade and finally give recommendation on the basis of final neighbor similarity and prediction grade.
作者
李智琦
陈世颖
杨怡凝
LI Zhi-qi, CHEN Shi-ying, YANG Yi-ning （School of Information Security Engineering, Shanghai Jiao Tong University, Shanghai 200240, China）
出处
《电脑知识与技术》
2011年第10期6828-6830,共3页
Computer Knowledge and Technology
关键词
数据挖掘
SNS
社交网络
个性化推荐
协同过滤
data mining
SNS
social network site
personalized recommendation
collaborative filtering
