结合半监督AP聚类和改进相似度的推荐算法

Recommendation Algorithm Combining Semi-supervised AP Clustering and Improved Similarity

寻找近邻用户或近邻项目是传统协同过滤推荐算法的关键内容.通常,数据稀疏性会导致推荐精度降低.基于项目类别偏好的混合协同过滤算法利用项目特征的低维性与二值性进行聚类,通过用户的类别偏好信息寻找近邻用户,此类方法可以在一定程度上缓解数据稀疏性问题.为了进一步提高近邻用户间的相似性,本文在项目类别偏好的混合协同过滤的算法基础上利用半监督AP聚类算法代替传统的聚类算法,并对相似性度量方式进行改进,提出了一种基于半监督AP聚类和改进用户相似度的协同过滤算法.该算法有两个方面改进:一方面,提出了一种新的半监督AP聚类算法-基于k近邻密度估计的半监督AP聚类;另一方面,使用用户活跃因子和用户评分轨迹改进Pearson相似度.实验结果证明了该算法的有效性.

It is the key content of traditional collaborative filtering recommendation algorithm to search for neighbor users or neighbor items.In general,data sparsity leads to a decrease in recommendation accuracy.Hybrid collaborative filtering algorithm based on project category preference uses the low-dimensional and binary features of project characteristics for clustering,and uses the category preference information of users to search for nearby users,this kind of method can alleviate the problem of data sparsity to some extent.In order to further improve the similarity between neighboring users,semi-supervised AP clustering algorithm is used to replace the traditional clustering algorithm based on the hybrid collaborative filtering algorithm based on project category preference,and the similarity measurement method is improved.A collaborative filtering algorithm based on semi-supervised AP clustering and improved user similarity is proposed in this paper.This algorithm has two improvements:on the one hand,a new semi-supervised AP clustering algorithm based on k-nearest neighbor density estimation is proposed;on the other hand,Pearson similarity was improved by using user activity factors and user scoring trajectories.Experimental results show the effectiveness of the algorithm.