基于粗糙集的协同过滤算法研究

Research on collaborative filtering algorithm based on rough set

协同过滤的推荐性能受限于评分矩阵中数据的稀疏性。针对这个问题,提出一种基于粗糙集的协同过滤方法,能在一定程度上缓解数据稀疏性的影响。首先进行数据预处理,即利用用户评分数量以及用户的评分值,作为用户进行分类的特征值,对用户进行分类;其次,利用粗糙集属性约简的方法剔除对用户分类影响较小的项目,生成更小的用户-项目评分矩阵,以降低数据的稀疏性和规模;最后基于约简后的数据集进行用户相似度的计算,获得目标用户真正近邻。实验结果表明,所提算法在常用评价推荐性能的指标MAE,COVERAGE,PRECISION和RECALL中显著优于COS,PCC,ADCOS,NHSM算法中的指标。

In collaborative filtering,the recommendation performance is limited by the data sparsity of rating matrix. To alleviate this problem,a rough set based collaborative filtering method is proposed,which is helpful to reduce the influence of the data sparsity to some extent. First,the procedure of data pre-processing is executed. As the eigenvalue for user classification,user’s ratings with the rating number are employed. Then,an attribute reduction approach of rough set is introduced to eliminate the item which has little effect with respect to user classification,and a smaller scale user-item rating matrix is generated to lower the data sparsity.Finally,the similarity between users is calculated based on the reducing attributes to obtain real neighbors of active users. The experimental results demonstrate that the proposed algorithm in MAE,COVERAGE,PRECISION and RECALL are significantly better than the ones of COS,PCC,ADCOS,NHSM.