Shandong Women's University, Jinan, China.
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China.
Sci Rep. 2022 Jan 7;12(1):252. doi: 10.1038/s41598-021-03871-y.
Click-through rate prediction, which aims to predict the probability of the user clicking on an item, is critical to online advertising. How to capture the user evolving interests from the user behavior sequence is an important issue in CTR prediction. However, most existing models ignore the factor that the sequence is composed of sessions, and user behavior can be divided into different sessions according to the occurring time. The user behaviors are highly correlated in each session and are not relevant across sessions. We propose an effective model for CTR prediction, named Session Interest Model via Self-Attention (SISA). First, we divide the user sequential behavior into session layer. A self-attention mechanism with bias coding is used to model each session. Since different session interest may be related to each other or follow a sequential pattern, next, we utilize gated recurrent unit (GRU) to capture the interaction and evolution of user different historical session interests in session interest extractor module. Then, we use the local activation and GRU to aggregate their target ad to form the final representation of the behavior sequence in session interest interacting module. Experimental results show that the SISA model performs better than other models.
点击率预测旨在预测用户点击某个项目的概率，这对在线广告至关重要。如何从用户行为序列中捕获用户不断变化的兴趣是 CTR 预测中的一个重要问题。然而，大多数现有模型忽略了序列是由会话组成的因素，并且可以根据发生时间将用户行为分为不同的会话。每个会话中的用户行为高度相关，而跨会话则不相关。我们提出了一种用于 CTR 预测的有效模型，名为通过自注意力的会话兴趣模型（Session Interest Model via Self-Attention，SISA）。首先，我们将用户的顺序行为划分为会话层。使用带偏差编码的自注意力机制来对每个会话进行建模。由于不同的会话兴趣可能相互关联或遵循顺序模式，因此接下来，我们在会话兴趣提取器模块中使用门控循环单元（GRU）来捕获用户不同历史会话兴趣之间的交互和演化。然后，我们使用局部激活和 GRU 来聚合他们的目标广告，以在会话兴趣交互模块中形成行为序列的最终表示。实验结果表明，SISA 模型的性能优于其他模型。
