论文标题
在推荐中重新享用点击率
Reweighting Clicks with Dwell Time in Recommendation
论文作者
论文摘要
点击行为是建议中使用最广泛的用户积极反馈。但是，仅考虑培训中的每次点击，可能会遭受点击诱饵和标题包含不匹配，因此无法精确捕获用户对项目的真正满意度。在每次点击时，停留时间可以视为用户偏好的高质量定量指标，而现有建议模型并未完全探索停留时间的建模。在这项工作中，我们专注于在推荐时重新加权点击。确切地说，我们首先定义了一种名为有效读取的新行为，该行为有助于通过停留时间为不同用户和项目选择高质量的点击实例。接下来，我们提出一个归一化的停留时间功能，以在培训中重新调整点击信号以供推荐。点击重新释放模型在现实世界系统中的离线和在线评估方面都取得了重大改进。
The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title-content mismatching, and thus fail to precisely capture users' real satisfaction on items. Dwell time could be viewed as a high-quality quantitative indicator of user preferences on each click, while existing recommendation models do not fully explore the modeling of dwell time. In this work, we focus on reweighting clicks with dwell time in recommendation. Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time. Next, we propose a normalized dwell time function to reweight click signals in training for recommendation. The Click reweighting model achieves significant improvements on both offline and online evaluations in real-world systems.
