Similitude Attentive Relation Network for Click-Through Rate Prediction
Similitude Attentive Relation Network for Click-Through Rate Prediction
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DOI：
10.1109/ijcnn48605.2020.9207521
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Hangyu Deng;Yulong Wang;Jia Luo;Jinglu Hu
中科院分区：
文献类型：
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作者：
Hangyu Deng;Yulong Wang;Jia Luo;Jinglu Hu
在在线广告系统中，充分了解用户行为对于点击率 (CTR) 预测至关重要。近年来，许多研究人员转而通过使用循环神经网络（RNN）对行为序列进行建模来寻求更好的用户表示方式。然而，循环层隐含地采用了不同阶的元素本质上不同的假设，这在许多不确定性和隐藏状态复杂的实际场景中效率低下。在本文中，我们遵循关系网络（RN）的范式，提出了一种称为相似性注意关系网络（SARN）的新模型。用户行为被建模为图表，其中节点对应于访问的项目，边对应于关系。为了更好地捕捉潜在的用户兴趣，该模型专注于项目之间的关系，而不是时间序列上的翻译。更具体地说，该模型试图通过可学习的点积运算来学习语义空间中项目之间的相似性，并将项目表示和关系信息混合在一起作为最终关系。我们直接在关系的集中池上定义我们的用户表示。为了验证我们方法的有效性，对两个公共数据集和一个真实世界的在线广告数据集进行了广泛的实验。实验结果表明，我们的方法通常比其他方法取得更好的性能。此外，我们通过受控实验探索模型的特性，并通过可视化 SARN 的内部状态来展示学到的关系知识。
In online advertising systems, having a good knowledge of user behavior is crucial for click-through rate (CTR) prediction. In recent years, many researchers turn to seek a better way of user representation by modeling the behavior sequences with recurrent neural network (RNN). However, recurrent layers implicitly adopt the assumption that elements with different orders are fundamentally different, which is inefficient in many practical scenarios with much uncertainty and complicated hidden states. In this paper, we follow the paradigm of Relation Network (RN), and propose a new model called Similitude Attentive Relation Network (SARN). The user behavior is modeled as a graph, where nodes correspond to the visited items and edges correspond to the relations. To capture the latent user interest better, the model concentrates on the relations between items, rather than the translation on the time series. More specifically, the model tries to learn the similarity between items in a semantic space through a learnable dot-product operation and blend both of the item representations and relational information together as the final relations. We define our user representation on an attentive pooling of the relations directly. To verify the effectiveness of our method, extensive experiments on two public datasets and one real-world online advertising dataset are conducted. Experimental results show that our methods achieve usually better performance than others. Besides, we explore the properties of our model by controlled experiments and show the learned relational knowledge by visualizing the inner states of SARN.