二阶有向相似性对协同过滤算法的影响

考虑用户的二阶相似性信息,提出了一种改进的协同过滤个性化推荐算法.实证统计发现,经典的基于产品映射的用户相似性定义中包含很多流行产品的信息,因此,无法准确度量用户的兴趣关联,通过引入有向的二阶相似性,算法可以有效降低大众主流喜好对目标用户相似性定义的影响.Movielens数据集上的实验结果显示,算法的准确度可以达到0.080 8,相对于经典的协同过滤算法,其准确性提高了22.08%,且当推荐列表长度L=50时,推荐列表的多样性可以达到0.775,较经典的协同过滤算法提高了10.87%.研究表明,二阶有向相似性信息对个性化推荐算法有很大影响.

Effect of Directive Second-order Similarity on Collaborative Filtering Recommender

An improved collaborative filtering(CF) recommendation algorithm based on the second-order similarity between users was introduced.According to the empirical statistics,it was found that the definition of classical item-projection-based similarity contains so plenty of information of popular items that the users' interests or preferences are hard to be measured.Owing to considering the directive second-order similarity,the new algorithm can effectively depress the influence of mainstream preferences on target user.The numerical results on one benchmark dataset Movielens show that the accuracy can reach 0.080 8,which is improved by 22.08% comparing with the standard CF.Correspondingly,the diversity reaches 0.775 and could be improved by 10.87% in the optimal case when the recommendation list equals to 50.The study indicates that the directive second-order similarity are crucial in recommendation algorithm.