Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit Information
Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit Information
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DOI：
10.1109/tnnls.2022.3182942
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发表时间：
2022-06
影响因子：
10.4
通讯作者：
Xiaoyao Zheng;Zhen Ni;Xiangnan Zhong;Yonglong Luo
中科院分区：
文献类型：
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作者：
Xiaoyao Zheng;Zhen Ni;Xiangnan Zhong;Yonglong Luo
在当前的矩阵因素建议方法中，项目和用户潜在因子向量具有相同的维度，即线性点产品被用作用户和项目之间的交互功能来预测评分之间的关系。真实的用户和项目并不完全是线性的，而矩阵分解的现有推荐模型面临着数据稀疏的挑战。矩阵以列向量的形式，并将其投影到更高的维度，以支持非线性用户的仿真 - 以增强用户和项目之间的连接，使用了协会规则的算法来挖掘用户和项目之间的隐式关系。 ，而不是简单的用户或项目的功能提取，而是通过自动编码器和内核网络处理进行第三名，以改善建议性能，而是由多层perceptron网络与迭代数据相连关于简单的加权总结，与几种现有的实验结果相比，在四个公共数据集上进行了预测的评级。数据稀疏性和预测准确性。
In the current matrix factorization recommendation approaches, the item and the user latent factor vectors are with the same dimension. Thus, the linear dot product is used as the interactive function between the user and the item to predict the ratings. However, the relationship between real users and items is not entirely linear and the existing recommendation model of matrix factorization faces the challenge of data sparsity. To this end, we propose a kernelized deep neural network recommendation model in this article. First, we encode the explicit user—item rating matrix in the form of column vectors and project them to higher dimensions to facilitate the simulation of nonlinear user—item interaction for enhancing the connection between users and items. Second, the algorithm of association rules is used to mine the implicit relation between users and items, rather than simple feature extraction of users or items, for improving the recommendation performance when the datasets are sparse. Third, through the autoencoder and kernelized network processing, the implicit data are connected with the explicit data by the multilayer perceptron network for iterative training instead of doing simple linear weighted summation. Finally, the predicted rating is output through the hidden layer. Extensive experiments were conducted on four public datasets in comparison with several existing well-known methods. The experimental results indicated that our proposed method has obtained improved performance in data sparsity and prediction accuracy.