改进Apriori算法在社交网络好友推荐中的应用

Application of Improved Apriori Algorithm in Social Network Friends Recommendation

针对Apriori算法在频繁项集自连接中产生大量的候选项集以及多次扫描数据库的不足,提出了一种改进的算法,该算法将数据库映射到一个布尔矩阵中,在矩阵列向量进行"与"运算之后,删除那些没有意义的项和记录,改进的算法在时间复杂度和空间复杂度上都有很大的提高.将改进的算法运用到社交网络好友推荐算法中,将网络社交平台中用户关注的用户和信息作为记录,将关注的用户作为交易项,构建交易数据库,计算频繁2项集,推荐按支持数排序的前N位用户作为好友.通过实验验证,改进的算法在社交网络好友推荐中具有较高的准确率和召回率.

Considering the limits that the Apriori algorithm produces numerous candidate itemsets during the self-joins of frequent items and scans database time after time, this paper proposed an improved algorithm. This algorithm maps the database to a boolean matrix, and then, deletes those meaningless items and records after the AND operation between matrix columns. This will greatly reduce the time and space complexities. Applying to the friend recommendation algorithm in social networks, this improved algorithm regards the interested users and information as records, takes the concerned users as deal items, builds a transaction database, computes frequent 2-item sets and recommends Top-N users ranked by supporting number as friends. The experiment proves the improved algorithm has higher precision and recall in friend recommendation algorithms of social networks.