基于项目层次相似性的推荐算法  

Recommendation algorithm based on hierarchical item similarity

作　　者：孙远帅[1] 陈垚[1] 刘向荣[1,2] 陈珂[3] 林琛[1,2] 

机构地区：[1]厦门大学计算机科学系,福建厦门361005 [2]厦门大学深圳研究院,广东深圳518057 [3]广东石油化工学院计算机科学与技术系,广东茂名525000

出　　处：《山东大学学报（工学版）》2014年第3期8-14,共7页Journal of Shandong University（Engineering Science）

基　　金：国家自然科学基金资助项目(61370010;61102136);福建省自然科学基金资助项目(2011J05158;2010J01350);深圳市科技信息基础研究计划资助项目(JC201006030858A;JCYJ20120618155655087)

摘　　要：针对协同过滤算法推荐效果依赖于相似度度量方法的问题,提出了一种基于项目层次结构相似度的推荐算法REHIS(recommendation hierarchical similarity)。首先利用关联规则挖掘和KNN(K nearest neighbor)算法完善项目层次结构,然后利用TopK算法计算项目之间的相似度,最后利用基于项目的协同过滤算法框架预测用户评分。为解决协同过滤算法扩展性差的问题,还把TopK算法推广到余弦距离和皮尔逊相关系数等常见的相似度度量方法。实验结果表明,与传统的协同过滤算法相比,REHIS能够获得更优的均方根误差,TopK算法可以减少最近邻项目的查找时间。To solve the problem that CF（Collaborative Filtering） recommendation highly depends on the accurate similarity measurement, a novel recommendation algorithm based on item hierarchy similarity was proposed, which was named REHIS（Recommendation Hierarchical Similarity）.The framework of REHIS was described as follows.First, the mining association rules and KNN （K Nearest Neighbor） algorithm were used to complement the hierarchy structure.Afterwards, the TopK method was employed to compute the similarity between items.Finally, scores were pre-dicted by using the framework of item-based CF algorithm.On the other hand, to solve the CF poor scalability problem, the TopK algorithm were further extended to the cosine distance and Pearson correlation coefficient, both of which were commonly used similarity measurement methods.Experimental results showed that, compared with existing algorithms, REHIS could achieve a better recommendation in term of root mean square error, and TopK could reduce the time cost for searching the most similar items, too.

关 键 词：推荐系统 协同过滤 TopK 标签 项目层次 倒排索引 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]