In modern online advertising systems, the click-through rate (CTR) is an important index to measure the popularity of an item. It refers to the ratio of users who click on a specific advertisement to the number of total users who view it. Predicting the CTR of an item in advance can improve the accuracy of the advertisement recommendation. And it is commonly calculated based on users’ interests. Thus, extracting users’ interests is of great importance in CTR prediction tasks. In the literature, a lot of studies treat the interaction between users and items as sequential data and apply the recurrent neural network (RNN) model to extract users’ interests. However, these solutions cannot handle the case when the sequence length is relatively long, e.g., over 100. This is because of the vanishing gradient problem of RNN, i.e., the model cannot learn a users’ previous behaviors that are too far away from the current moment. To address this problem, we propose a new Core Interest Network (CIN) model to mitigate the problem of a long sequence in the CTR prediction task with sequential data. In brief, we first extract the core interests of users and then use the refined data as the input of subsequent learning tasks. Extensive evaluations on real dataset show that our CIN model can outperform the state-of-the-art solutions in terms of prediction accuracy.

中文翻译：

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点击率预测的核心兴趣网络

在现代在线广告系统中，点击率（CTR）是衡量一个项目受欢迎程度的重要指标。它是指点击特定广告的用户与查看该广告的用户总数的比率。提前预测商品的点击率可以提高广告推荐的准确性。它通常是根据用户的兴趣来计算的。因此，提取用户兴趣在点击率预测任务中非常重要。在文献中，许多研究将用户和物品之间的交互视为序列数据，并应用循环神经网络（RNN）模型来提取用户的兴趣。但是，这些解决方案无法处理序列长度相对较长的情况，例如超过 100。这是因为 RNN 的梯度消失问题，即 该模型无法学习用户之前与当前时刻相距太远的行为。为了解决这个问题，我们提出了一种新的核心兴趣网络 (CIN) 模型，以缓解具有序列数据的 CTR 预测任务中的长序列问题。简而言之，我们首先提取用户的核心兴趣，然后将提炼的数据作为后续学习任务的输入。对真实数据集的广泛评估表明，我们的 CIN 模型在预测准确性方面可以胜过最先进的解决方案。我们首先提取用户的核心兴趣，然后将提炼的数据作为后续学习任务的输入。对真实数据集的广泛评估表明，我们的 CIN 模型在预测准确性方面可以胜过最先进的解决方案。我们首先提取用户的核心兴趣，然后将提炼的数据作为后续学习任务的输入。对真实数据集的广泛评估表明，我们的 CIN 模型在预测准确性方面可以胜过最先进的解决方案。