欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
针对传统专家列表排序方法易陷入局部最小和训练时间过长、不能较好逼近排序函数的问题，结合深度神经网络与Listwise的专家排序方法，提出基于Listwise的深度学习专家排序方法．该方法首先提出深度学习专家排序模型，通过无监督的自训练得到较优参数逐层初始化权重．再将查询对应的专家文档形成的训练实例输入到受限玻尔兹曼机中进行训练，通过余弦值取代矩阵相减计算权重，完成权重整体更新，构建深度学习专家排序模型．对比实验表明文中方法具有较好效果，引入深度学习能有效提升排序精度．
英文摘要：
The traditional expert list ranking method is easy to fall into local minimum, its training time is long, and the ranking function can not be approximated well. Combining listwise expert ranking with deep neural network, a deep learning expert ranking method based on listwise is proposed. Firstly, a deep learning expert ranking model is presented. Through unsupervised self-training, better parameters are obtained to initialize weights layer by layer. Then, the training instances formed by the expert documents corresponding to the queries are inputted into the restricted Bohzmann machines for the training. Finally, cosine value is used instead of matrix subtraction to compute weight. Thus, the whole replacement of weights is finished and the deep learning expert ranking model is constructed. The comparative experiments of expert ranking show that the proposed method is efficient and it improves the accuracy of ranking effectively.
同期刊论文项目
同项目期刊论文
Restricted domain question-answering text retrieval method based on supervised latent dirichlet allo
Approaches to Detect Micro-blog User Interest Communities through theIntegration of Explicit User Re
期刊信息
位置：