User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and user behaviour from review text. Deep Cooperative Neural Network (DeepCoNN) is the suggested model consisting of two parallel neural networks connected in their final layers. One of the networks focuses on learning user behaviour from reviews submitted by the user, while the other network learns item attributes from user reviews. On top, a shared layer is added to connect these two networks. Similar to factorization machine approaches, the shared layer allows latent factors acquired for people and things to interact with each other. On a number of datasets, DeepCoNN surpasses all baseline recommendation systems, according to experimental findings.

翻译：用户评价包括大量在线平台的信息。 这一信息来源被大多数现有建议系统忽视,尽管它有可能缓解宽度问题和提高建议质量。 这项工作为同时从审查文本中学习项目属性和用户行为提供了一个深层模型。 深合作神经网络(DeepCONN)是建议的模式,由两个平行神经网络在最后一层连接起来。 其中一个网络侧重于从用户提交的审查中学习用户行为,而另一个网络则从用户审查中学习项目属性。 最重要的是,为连接这两个网络添加了一个共享层。 与保分机方法相似, 共享层允许为人和物获取的潜在因素相互作用。 根据实验结果, 在许多数据集中, DeepCONN超越了所有基线建议系统。