The recommendation has been playing a key role in many industries, e.g., e-commerce, streaming media, social media, etc. Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e.g., Alibaba.com and Amazon. Without explicitly modeling the user's instant interest, traditional recommendation methods usually obtain sub-optimal results in TIR. Even though there are a few methods considering the trigger and target items simultaneously to solve this problem, they still haven't taken into account temporal information of user behaviors, the dynamic change of user instant interest when the user scrolls down and the interactions between the trigger and target items. To tackle these problems, we propose a novel method -- Deep Evolutional Instant Interest Network (DEI2N), for click-through rate prediction in TIR scenarios. Specifically, we design a User Instant Interest Modeling Layer to predict the dynamic change of the intensity of instant interest when the user scrolls down. Temporal information is utilized in user behavior modeling. Moreover, an Interaction Layer is introduced to learn better interactions between the trigger and target items. We evaluate our method on several offline and real-world industrial datasets. Experimental results show that our proposed DEI2N outperforms state-of-the-art baselines. In addition, online A/B testing demonstrates the superiority over the existing baseline in real-world production environments.

翻译：推荐已在众多行业中扮演关键角色，例如电子商务、流媒体、社交媒体等。近年来，一种名为触发诱导推荐（Trigger-Induced Recommendation, TIR）的新型推荐场景逐渐兴起，用户可通过触发项显式表达其即时兴趣，在阿里巴巴、亚马逊等电商平台中发挥着重要作用。传统推荐方法因未显式建模用户即时兴趣，在TIR中通常只能获得次优结果。尽管已有部分方法同时考虑触发项和目标项来解决该问题，但仍未考虑用户行为的时间信息、用户滚动时即时兴趣的动态变化，以及触发项与目标项之间的交互。为解决上述问题，我们提出一种新方法——深度演化即时兴趣网络（Deep Evolutional Instant Interest Network, DEI2N），用于TIR场景下的点击率预测。具体而言，我们设计了用户即时兴趣建模层，用于预测用户滚动过程中即时兴趣强度的动态变化；在用户行为建模中引入时间信息；并引入交互层以学习触发项与目标项之间更优的交互。我们在多个离线数据集及真实工业数据集上评估了该方法。实验结果表明，我们所提出的DEI2N优于当前最先进的基线模型。此外，在线A/B测试验证了其在真实生产环境中相较现有基线的优越性。