论文标题
循环审查模型
Recurrent Point Review Models
论文作者
论文摘要
深度神经网络模型代表了自然语言处理的最新方法。在这里，我们以这些方法为基础，以合并时间信息并建模如何随时间而变化。具体来说，我们使用经常性点过程模型的动态表示，该表示及时地编码了如何收到业务或服务评论的历史记录，以生成具有改进预测功能的瞬时语言模型。同时，我们的方法学通过合并汇总的审核内容表示来增强我们的点过程模型的预测能力。我们提供反复的网络和时间卷积解决方案，用于建模审核内容。我们在推荐系统的背景下部署方法，有效地表征了随着时间的发展，用户的偏好和品味的变化。源代码可在[1]上找到。
Deep neural network models represent the state-of-the-art methodologies for natural language processing. Here we build on top of these methodologies to incorporate temporal information and model how to review data changes with time. Specifically, we use the dynamic representations of recurrent point process models, which encode the history of how business or service reviews are received in time, to generate instantaneous language models with improved prediction capabilities. Simultaneously, our methodologies enhance the predictive power of our point process models by incorporating summarized review content representations. We provide recurrent network and temporal convolution solutions for modeling the review content. We deploy our methodologies in the context of recommender systems, effectively characterizing the change in preference and taste of users as time evolves. Source code is available at [1].
