计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 103-109.doi: 10.11896/jsjkx.190500183
李太松1,2,贺泽宇1,2,王冰1,2,颜永红1,2,3,唐向红4
LI Tai-song1,2,HE Ze-yu1,2,WANG Bing1,2,YAN Yong-hong1,2,3,TANG Xiang-hong4
摘要: 针对循环神经网络(Recurrent Neural Network,RNN)模型在序列流推荐中只能从宏观上捕捉序列的演变模式,忽略了物品(Item)间内部的微观联系,无法长程建模序列数据的变化规律的问题,提出了多维度序列建模算法循环时间卷积网络(Recurrent Temporal Convolutional Network,RTCN)。首先,将每个物品表示成定长向量,采用多层因果卷积和扩张卷积操作扩大感受野范围,建立序列元素间的长程依赖关系。利用残差连接网络提取不同层次的特征信息,解决反向传播中梯度衰减甚至消失的问题。综合设计时间卷积网络(Temporal Convolutional Network,TCN)提取序列流中前后物品间的局部特征,将物品信息映射到隐藏空间,得到细粒度的特征向量。为进一步建立元素间的宏观联系,将特征向量依次输入门限循环单元(Gated Recurrent Unit,GRU),迭代更新现有隐藏状态并预测下一时刻的输出。RTCN通过时间卷积网络,从输入序列流提取出长时间、多维度、细粒度的局部关联特征;经过门限循环网络,建模序列间的长距离依赖关系,捕捉序列元素的演变模式,并预测下一个出现的物品。利用网站、手机应用和音乐3个不同场景中的数据对模型进行了实验。实验结果显示,RTCN模型在召回率(Recall)和平均排序倒数(MRR)两个指标上比RNN模型高出6%~13%,比传统推荐算法高出9%~59%。通过对比不同的损失函数,模型在交叉熵损失函数下表现最优。此外,由于TCN中的卷积层具有多通道的结构,当数据维度丰富时,该模型对物品和用户的上下文信息具有很强的综合能力。
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