School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
Computational Aerodynamics Institute at China Aerodynamics Research and Development Center, Mianyang, China.
Math Biosci Eng. 2022 Jul 27;19(10):10673-10686. doi: 10.3934/mbe.2022499.
With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text information and the item text information are input to the embedding layer to change the text information into numerical vector. The convolutional neural network is used to get the latent feature vector of the text information. The latent vector is connected to rating vector to get the item and the user vector. The user vector and the item vector are fused together to get the user-item matrix which is input to the visual layer of Gaussian restricted Boltzmann Machine to predict the ratings. Some simulation experiments have been performed on the algorithm, and the results of the experiments proved that the algorithm is feasible.
随着大数据的空前发展，获取有价值的信息变得越来越困难，因此推荐系统变得越来越流行。当使用有限的玻尔兹曼机进行协同过滤时，仅考虑评分矩阵，而不考虑项目内容、用户特征和用户评价内容对预测得分的影响。为了解决这个问题，本文提出了一种基于高斯受限玻尔兹曼机的改进混合推荐算法。将用户文本信息和项目文本信息输入到嵌入层，将文本信息转换为数字向量。使用卷积神经网络获取文本信息的潜在特征向量。将潜在向量与评分向量连接，以获得项目和用户向量。将用户向量和项目向量融合在一起，得到输入到高斯受限玻尔兹曼机的视觉层的用户-项目矩阵，以预测评分。对算法进行了一些仿真实验，实验结果证明了该算法的可行性。
