Deep learning based recommendation methods, such as the recurrent neural network based recommendation method (RNNRec) and the gated recurrent unit (GRU) based recommendation method (GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.

中文翻译：

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基于深度学习的推荐中的用户空间转换

提出了基于深度学习的推荐方法，如基于循环神经网络的推荐方法（RNNRec）和基于门控循环单元（GRU）的推荐方法（GRURec），以解决时间异构反馈推荐问题。这些方法优于几种最先进的方法。然而，在 RNNRec 和 GRURec 中，动作向量和项目向量在用户之间共享。不考虑同一动作对不同用户的不同含义。同样，用户对同一项目的不同偏好也被忽略。为了解决这个问题，本文修改了RNNRec和GRURec的模型。在所提出的方法中，首先将动作向量和项目向量转换为每个用户的用户空间，然后将变换后的向量输入到原始的 RNNRec 和 GRURec 神经网络中。变换后的动作向量和项目向量代表了用户指定的动作含义和对项目的偏好，这使得所提出的方法获得更准确的推荐结果。在两个真实数据集上的实验结果表明，在大多数情况下，所提出的方法优于 RNNRec 和 GRURec 以及其他最先进的方法。