Deep learning has been successfully applied in the recommender system. Low-dimensional dense embedding is typically used to represent the feature of users and items. To optimize the model, some models propose to dynamically search the embedding size based on the popularity of different users and items. However, these models ignore the interaction between the user and the item which will hinder the optimization of the features in embedding. In this paper, we propose Object-aware Policy Network (OPN) and introduces an object-aware method that is used for optimizing the features in embedding. We evaluate our model on the two real-world benchmark datasets. With less than 10% increased time consumption in all experiments, the results show that our proposed model is able to improve the performance of binary classification task by a margin of 0.30 and multiclass classification task by a margin of 0.35 compared to the best accuracies achieced by baselines on different datasests.

深度学习已成功应用于推荐系统。低维密集嵌入通常用于表示用户和物品的特征。为了优化模型，一些模型建议根据不同用户和项目的流行度动态搜索嵌入大小。然而，这些模型忽略了用户与项目之间的交互，这将阻碍嵌入中特征的优化。在本文中，我们提出了对象感知策略网络（OPN），并介绍了一种用于优化嵌入特征的对象感知方法。我们在两个真实世界的基准数据集上评估我们的模型。在所有实验中，时间消耗增加不到 10%，结果表明我们提出的模型能够将二元分类任务的性能提高 0 倍。