论文标题
基于改进的Convmf
Bi-convolution matrix factorization algorithm based on improved ConvMF
论文作者
论文摘要
随着信息技术的快速发展，“信息过载”已成为困扰人们在线生活的主要主题。作为帮助用户快速搜索有用信息的有效工具，个性化的建议在人们中越来越受欢迎。为了解决传统矩阵分解算法的稀疏问题以及审查文档信息的低利用率问题，本文提出了基于改进的Convmf的BICON-VMF算法。该算法使用两个平行的卷积神经网络从用户审核集和项目评论集中提取深度功能，并将这些功能融合到额定值矩阵的分解中，以便更准确地构建用户潜在模型和项目潜在模型。实验结果表明，与PMF，Convmf和DeepConn（Convmf和DeepConn）等传统推荐算法相比，本文提出的方法的预测错误较低，并且可以达到更好的建议效应。具体而言，与前三种算法相比，本文提出的算法的预测错误分别减少了45.8％，16.6％和34.9％。
With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life. As an effective tool to help users quickly search for useful information, a personalized recommendation is more and more popular among people. In order to solve the sparsity problem of the traditional matrix factorization algorithm and the problem of low utilization of review document information, this paper proposes a Bicon-vMF algorithm based on improved ConvMF. This algorithm uses two parallel convolutional neural networks to extract deep features from the user review set and item review set respectively and fuses these features into the decomposition of the rating matrix, so as to construct the user latent model and the item latent model more accurately. The experimental results show that compared with traditional recommendation algorithms like PMF, ConvMF, and DeepCoNN, the method proposed in this paper has lower prediction error and can achieve a better recommendation effect. Specifically, compared with the previous three algorithms, the prediction errors of the algorithm proposed in this paper are reduced by 45.8%, 16.6%, and 34.9%, respectively.
