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.

翻译：点击行为是推荐中最广泛使用的用户正反馈信号。然而，在训练过程中同等对待每次点击可能会导致点击诱饵和标题-内容不匹配问题，从而无法精确捕捉用户对项目的真实满意度。停留时间可作为用户对每次点击偏好的高质量量化指标，但现有推荐模型尚未充分探索停留时间的建模方式。本文聚焦于推荐中基于停留时间的点击重加权方法。具体而言，我们首先定义了一种名为"有效阅读"的新行为，通过停留时间为不同用户和项目筛选高质量点击实例。其次，我们提出一种归一化停留时间函数，用于在推荐训练中对点击信号进行重加权。该点击重加权模型在实际系统的离线与在线评估中均取得了显著性能提升。