基于切比雪夫一阶截断式的谱卷积协同过滤

Collaborative filtering of spectral convolution based on Chebyshev first-order truncation

为解决个性化推荐协同过滤中存在用户关联项目过少,而引起的用户冷启动问题,提出一种基于切比雪夫优化的谱卷积协同过滤推荐算法。将用户-项目二部图转换到谱域,通过切比雪夫一阶截断式建立深度前馈神经网络,优化卷积过程,省略拉普拉斯矩阵复杂的特征分解,缩短模型训练时间,在谱域中快速发现用户与相关项目之间的隐性关联信息。经过实验验证,该方法对提升推荐结果的准确性有着较为明显的帮助,更为有效挖掘用户与项目间关联信息。

To solve the problem of cold start of users caused by few user-associated items in personalized recommendation collaborative filtering,a recommendation algorithm based on Chebyshev optimization of spectral convolution collaborative filtering was proposed.The user-item bipartite graph was transferred into a spectral domain.Through Chebyshev first-order truncation,deep feedforward neural network was built,the convolution process was effectively optimized,complex eigenvalues of Laplacian model were omitted,the training time was shortened and the deep connection between the user and the project was found effectively in the domain.Experimental results show that the proposed method can improve the accuracy of the recommendation results and mine the associated information between users and projects more effectively.