| ID | 原文 | 译文 |
| 57158 | 为了解决推荐系统冷启动问题，对融合社交关系的推荐系统进行了研究，提出了贝叶斯个性化排序评论评分社交模型和可扩展的贝叶斯个性化排序评论评分社交模型，将评分、评论、社交关系等多源异构数据从数据源层面进行了融合，通过用户好友信任度模型将社交关系引入到推荐系统中，用基于段向量的分布式词袋模型处理评论，用全连接神经网络处理评分，用改进的贝叶斯个性化排序模型对排序结果进行优化. | In order to solve the cold start problem of the recommended system，a recommendation system that integrates social relation- ships is studied，and Bayesian personalized ranking review score social model and scalable Bayesian per- sonalized ranking review score social model are proposed. The proposed fusion recommendation models integrate multi-source heterogeneous data such as scores，reviews，and social relationships from the data source level，introduce social relationships into the recommendation system through the user friend trust model，use the paragraph vector-distributed memory model to process review，use the fully connected neural network to process rating，and use an improved Bayesian personalized ranking model to optimize the ranking results. |
| 57159 | 实验在 Yelp 公开数据集上进行了实验，实验结果表明，所提出的 2 种模型的推荐准确度均优于其他推荐模型. | Experiments are conducted on the Yelp public dataset. It is shown that the recom- mendation accuracy of the two proposed models are better than other recommendation models. |
| 57160 | 针对运动想象脑电信号处理中分类准确率较低的问题，提出了一种基于能量( 二阶矩) 小波包变换和莱文伯格－马夸特神经网络算法相结合的运动想象脑电信号处理方法. | Aiming at the classification accuracy of the motor imagery electroencephalogram in processing is low，a processing method based on the combination of energy ( second-order moment) wavelet packet transform and Levenberg-Marquardt neural network is proposed. |
| 57161 | 首先，利用能量方法对信号进行时域分析，选取有效的时序段; | Firstly，the energy method is used to ana- lyze signal in the time domain，and the effective time sequence is selected. |
| 57162 | 然后，使用小波包变换对所选有效时域段的各导信号进行时频分解，选取与想象任务相关的频段信息重构脑电信号特征; | Then，wavelet packet transform is used to decompose the time-frequency of each pilot signal in the selected effective time-domain seg- ment，and the frequency information related to the imagination task is selected to reconstruct the signal characteristics. |
| 57163 | 最后，将各导信号重构的特征串接，导入基于莱文伯格－马夸特训练算法的神经网络实现最终的任务分类. | Finally，the features reconstructed by each guide signal are concatenated and imported in- to the neural network based on the Levenberg-Marquardt training algorithm to realize the task classifica- tion. |
| 57164 | 利用 2 个脑电信号标准竞赛数据库进行方法验证，分别取得了 95. | The method was verified by two kinds of electroencephalogram signal standard competition database， and the classification accuracy is 95. |
| 57165 | 62% 和 90. | 62% and 90. |
| 57166 | 13% 的分类准确率. | 13% ，respectively. |
| 57167 | 与近期的一些研究成果进行对比，可知该方法具有较好的分类效果. | Compared with some recent re- search results，this algorithm has a better processing effect. |