摘要：

针对现有社交好友推荐算法只关注“人”忽略“事”的问题,提出一种基于关联规则与相似度的推荐算法。通过对用户每天发布的信息进行相似度计算,将相似度达到给定阈值的信息视为一条交易记录,把发布的信息视为交易项,信息库视为交易数据库,计算出二阶候选项集,推荐支持数最高的前N项信息的发布者为好友。实验结果表明,与基于关联规则与标签的好友推荐算法相比,该算法具有较高的准确率。

关键词: 好友推荐算法, 关联规则, 社交平台, 相似度, 交易数据库

Abstract:

Aiming at the problem that the existing social friend recommendation algorithm only focuses on people and ignores things,a recommendation algorithm based on association rules and similarity is proposed.By calculating the similarity of the information released by the user every day,the information with the similarity reaching the given threshold is regarded as a transaction record,the published information is regarded as the transaction item,the information database is regarded as the transaction database,and the second-order candidate set is calculated.It is recommended that the publisher who supports the top N items of the highest number of friends be friends.Experimental results show that compared with the friend recommendation algorithm based on association rules and labels,this algorithm has higher accuracy.

Key words: friend recommendation algorithm, association rules, social platform, similarity, transaction database

中图分类号: 

• TP391