论文标题
深层混合模型用于推荐
Deep Latent Mixture Model for Recommendation
论文作者
论文摘要
神经网络的最新进展已成功应用于在线推荐应用程序中的许多任务。我们提出了一个称为锥形混合模型的新框架，该框架利用手工制作的状态能够考虑多个相关文档之间的不同依赖性。具体而言，它使用判别优化技术来生成有效的多层次知识库，并使用在线判别学习技术来利用这些功能。对于此联合模型，该模型使用每个主题的置信度估算，并且能够学习经过歧视的训练，以自动提取显着特征，而歧视性训练仅使用功能，然后才能准确培训。
Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications. We propose a new framework called cone latent mixture model which makes use of hand-crafted state being able to factor distinct dependencies among multiple related documents. Specifically, it uses discriminative optimization techniques in order to generate effective multi-level knowledge bases, and uses online discriminative learning techniques in order to leverage these features. And for this joint model which uses confidence estimates for each topic and is able to learn a discriminatively trained jointly to automatically extracted salient features where discriminative training is only uses features and then is able to accurately trained.
