Shandong Women's University, Jinan, China.
Shandong Normal University, Jinan, China.
PLoS One. 2022 Aug 17;17(8):e0273048. doi: 10.1371/journal.pone.0273048. eCollection 2022.
Click-through rate prediction has become a hot research direction in the field of advertising. It is important to build an effective CTR prediction model. However, most existing models ignore the factor that the sequence is composed of sessions, and the user behaviors are highly correlated in each session and are not relevant across sessions. In this paper, we focus on user multiple session interest and propose a hierarchical model based on session interest (SIHM) for CTR prediction. First, we divide the user sequential behavior into session layer. Then, we employ a self-attention network obtain an accurate expression of interest for each session. Since different session interest may be related to each other or follow a sequential pattern, next, we utilize bidirectional long short-term memory network (BLSTM) to capture the interaction of different session interests. Finally, the attention mechanism based LSTM (A-LSTM) is used to aggregate their target ad to find the influences of different session interests. Experimental results show that the model performs better than other models.
点击率预测已成为广告领域的热门研究方向。构建有效的 CTR 预测模型非常重要。然而，大多数现有模型忽略了序列由会话组成的因素，并且每个会话中的用户行为高度相关，而不在会话之间相关。在本文中，我们专注于用户的多会话兴趣，并提出了一种基于会话兴趣的分层模型（SIHM）用于 CTR 预测。首先，我们将用户的顺序行为划分为会话层。然后，我们使用自注意力网络为每个会话获得准确的兴趣表达。由于不同的会话兴趣可能相互关联或遵循顺序模式，接下来，我们利用双向长短期记忆网络（BLSTM）来捕获不同会话兴趣之间的交互。最后，使用基于注意力机制的 LSTM（A-LSTM）来聚合它们的目标广告，以发现不同会话兴趣的影响。实验结果表明，该模型的性能优于其他模型。
