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 pro...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.展开更多
正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术凭借对多普勒频移的优良抗性,保证了高动态场景下的可靠性通信。与大多数OTFS信号检测方案相比,基于深度学习(Deep Learning, DL)的OTFS检测器不需要耗费高额的导频能量,以...正交时频空(Orthogonal Time Frequency Space, OTFS)调制技术凭借对多普勒频移的优良抗性,保证了高动态场景下的可靠性通信。与大多数OTFS信号检测方案相比,基于深度学习(Deep Learning, DL)的OTFS检测器不需要耗费高额的导频能量,以此获得精确的信道状态信息。基于多维输入的卷积神经网络(Convolutional Neural Networks, CNN)和一维输入的深度神经网络(Deep Neural Networks, DNN),搭建了OTFS信号检测模型,并结合OTFS的输入输出关系,以模型驱动,提出一种部分输入方法。与数据驱动DL相比,该方法沿时延轴截断输入数据,仅向网络输入与待检测信号相关性强的部分接收信号。该方法不仅减小了数据驱动CNN和DNN的训练参数量,降低了训练复杂度,而且检测性能也不弱于传统的线性最小均方误差(Linear Minimum Mean Square Error, LMMSE)算法。展开更多
基金supported by the National Natural Science Foundation of China(61403350)。
文摘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.
