论文标题
将用户和项目评论集成到深层合作神经网络中以进行电影推荐
Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation
论文作者
论文摘要
用户评估包括跨在线平台的大量信息。尽管大多数现有推荐系统都可以缓解稀疏性问题并提高建议质量，但大多数现有推荐系统都忽略了此信息源。这项工作为同时学习项目属性和用户行为提供了一个深层模型。深层合作神经网络（DeepConn）是建议的模型，该模型由两个平行的神经网络组成，这些神经网络在其最终层中相连。其中一个网络专注于从用户提交的评论中学习用户行为，而另一个网络从用户评论中学习项目属性。最重要的是，添加了共享层以连接这两个网络。与分解机方法相似，共享层允许获得的潜在因素和事物相互互动。根据实验发现，在许多数据集中，DeepConn超过所有基线推荐系统。
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.
