A time-aware self-attention based neural network model for sequential recommendation
A time-aware self-attention based neural network model for sequential recommendation
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DOI：
10.1016/j.asoc.2022.109894
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yihu Zhang;Bo Yang;Haodong Liu;Dong-Chi Li
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文献类型：
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作者：
Yihu Zhang;Bo Yang;Haodong Liu;Dong-Chi Li
序贯推荐是近年来的研究热点之一。已经提出了各种顺序推荐模型，其中基于自注意（SA）的模型被证明具有最先进的性能。然而，大多数现有的基于SA的顺序推荐模型没有利用时间信息，即，用户-项目交互的时间戳，除了初始尝试（Li等人，2020年）。在本文中，我们提出了一个时间感知的Transformer顺序推荐（TAT 4SRec），一个基于SA的神经网络模型，利用时间信息，更准确地捕捉用户的偏好。TAT 4SRec有两个显著的特点：（1）TAT 4SRec使用编码器-解码器结构分别对时间戳和交互项进行建模，这种结构似乎是一种更好的利用时间信息的方式。(2)在TAT 4SRec中，设计了两个不同的嵌入模块，分别将连续数据（时间戳）和离散数据（项目ID）转换为嵌入矩阵。具体来说，我们提出了一个基于窗口函数的嵌入模块，以保持包含在类似的时间戳的连续依赖。最后，大量的实验证明了所提出的TAT 4SRec的有效性在各种国家的最先进的MC/RNN/SA为基础的顺序推荐模型在几个广泛使用的指标。最后通过实验验证了所提出的不同结构的合理性，并验证了TAT 4SRec的计算效率。实验结果表明，TAT 4SRec可以应用于各种在线应用。
Sequential recommendation is one of the hot research topics in recent years. Various sequential recommendation models have been proposed, of which Self-Attention (SA)-based models are shown to have state-of-the-art performance. However, most of the existing SA-based sequential recommendation models do not make use of temporal information, i.e., timestamps of user–item interactions, except for an initial attempt (Li et al., 2020). In this paper, we propose a Time-Aware Transformer for Sequential Recommendation (TAT4SRec), an SA-based neural network model which utilizes the temporal information and captures users’ preferences more precisely. TAT4SRec has two salient features: (1) TAT4SRec utilizes an encoder–decoder structure to model timestamps and interacted items separately and this structure appears to be a better way of making use of the temporal information. (2) in the proposed TAT4SRec, two different embedding modules are designed to transform continuous data (timestamps) and discrete data (item IDs) into embedding matrices respectively. Specifically, we propose a window function-based embedding module to preserve the continuous dependency contained in similar timestamps. Finally, extensive experiments demonstrate the effectiveness of the proposed TAT4SRec over various state-of-the-art MC/RNN/SA-based sequential recommendation models under several widely-used metrics. Furthermore, experiments are also performed to show the rationality of the different proposed structures and demonstrate the computation efficiency of TAT4SRec. The promising experimental results make it possible to apply TAT4SRec in various online applications.