结合矩阵补全的宽度协同过滤推荐算法

Broad collaborative filtering recommendation algorithm combined with matrix completion

协同过滤是推荐系统中最经典的方法之一,能够满足人们对个性化推荐任务的需求,但许多协同过滤算法在面对评分数据稀疏性问题时推荐效果不佳。为解决此问题,提出一种结合矩阵补全的宽度协同过滤推荐算法。先使用矩阵补全技术对用户项目评分矩阵进行补全,再利用补全后的矩阵对已评分的用户和项目分别寻找其近邻项,进而构造用户与项目的评分协同向量,最后使用宽度学习系统来构建用户项目与评分之间的复杂的非线性关系。在MovieLens和filmtrust数据集上对所提出算法的有效性进行检验。试验结果表明,与当前最先进的方法相比,该方法能够有效地缓解数据稀疏性问题,具有较低的计算复杂度,在一定程度上提升了推荐系统的性能。

Collaborative filtering is a classic method used in recommendation systems,designed to cater to the need for personalized recommendations.However,many collaborative filtering algorithms struggle when confronted with sparse rating data.To address this issue,we propose a broad collaborative filtering recommendation algorithm that integrates matrix completion.Initially,a matrix completion technique is employed to recover the user–item rating matrix.Subsequently,this completed rating matrix is utilized to identify respective neighbors for a given user–item pair,which in turn helps create the user–item rating collaboration vector.Finally,a broad learning system is employed to establish the complex nonlinear relationship between user-items and ratings.The effectiveness of the proposed algorithm has been validated through tests on MovieLens and Filmtrust data sets.The experimental results show that,compared with state-of-the-art collaborative filtering methods,the proposed method can effectively alleviate the data sparsity problem.It also possesses lower computational complexity and enhances recommendation performance to a certain extent.