School of Art and Design, Qingdao University of Technology, Qingdao 266033, Shandong, China.
Comput Intell Neurosci. 2021 Nov 26;2021:4953288. doi: 10.1155/2021/4953288. eCollection 2021.
In the era of big data, the problem of information overload is becoming more and more obvious. A piano music image analysis and recommendation system based on the CNN classifier and user preference is designed by using the convolutional neural network (CNN), which can realize accurate piano music recommendation for users in the big data environment. The piano music recommendation system based on the CNN is mainly composed of user modeling, music feature extraction, recommendation algorithm, and so on. In the recommendation algorithm module, the potential characteristics of music are predicted by the regression model, and the matching degree between users and music is calculated according to user preferences. Then, music that users may be interested in is generated and sorted in order to recommend new piano music to relevant users. The image analysis model contains four "convolution + pooling" layers. The classification accuracy and gradient change law of the CNN under RMSProp and Adam optimal controllers are compared. The image analysis results show that the Adam optimal controller can quickly find the direction, and the gradient decreases greatly. In addition, the accuracy of the recommendation system is 55.84%. Compared with the traditional CNN algorithm, this paper uses the convolutional neural network (CNN) to analyze and recommend piano music images according to users' preferences, which can realize more accurate piano music recommendation for users in the big data environment. Therefore, the piano music recommendation system based on the CNN has strong feature learning ability and good prediction and recommendation ability.
在大数据时代，信息过载的问题越来越明显。本研究设计了一种基于卷积神经网络（CNN）分类器和用户偏好的钢琴音乐图像分析和推荐系统，旨在为大数据环境中的用户实现精准的钢琴音乐推荐。基于 CNN 的钢琴音乐推荐系统主要由用户建模、音乐特征提取、推荐算法等组成。在推荐算法模块中，通过回归模型预测音乐的潜在特征，并根据用户偏好计算用户与音乐的匹配度，进而生成并排序用户可能感兴趣的音乐，为相关用户推荐新的钢琴音乐。图像分析模型包含四个“卷积+池化”层。对比 RMSProp 和 Adam 最优控制器下 CNN 的分类准确率和梯度变化规律。图像分析结果表明，Adam 最优控制器能够快速找到方向，梯度大幅度下降。此外，推荐系统的准确率为 55.84%。与传统的 CNN 算法相比，本文根据用户偏好使用卷积神经网络（CNN）对钢琴音乐图像进行分析和推荐，能够为大数据环境中的用户实现更精准的钢琴音乐推荐。因此，基于 CNN 的钢琴音乐推荐系统具有较强的特征学习能力和良好的预测推荐能力。
