Predicting conversion rate (e.g., the probability that a user will purchase an item) is a fundamental problem in machine learning based recommender systems. However, accurate conversion labels are revealed after a long delay, which harms the timeliness of recommender systems. Previous literature concentrates on utilizing early conversions to mitigate such a delayed feedback problem. In this paper, we show that post-click user behaviors are also informative to conversion rate prediction and can be used to improve timeliness. We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as stochastic post-click information, which could be utilized to train GDFM in a streaming manner efficiently. Based on GDFM, we further establish a novel perspective that the performance gap introduced by delayed feedback can be attributed to a temporal gap and a sampling gap. Inspired by our analysis, we propose to measure the quality of post-click information with a combination of temporal distance and sample complexity. The training objective is re-weighted accordingly to highlight informative and timely signals. We validate our analysis on public datasets, and experimental performance confirms the effectiveness of our method.
翻译:预测转换率(例如,用户购买一个项目的概率)是机器学习基于建议者系统中的一个根本问题。然而,精确转换标签在长期拖延后会暴露出来,这会损害建议者系统的及时性。以前的文献侧重于利用早期转换来减轻这种延迟反馈问题。在本文中,我们显示,点击后用户的行为也为转换率预测提供了信息,并可用于提高及时性。我们提议了一个普遍的延迟反馈模式(GDFM),将点击后行为和早期转换统一为随机后点击后信息,可用于高效地培训GDFM。我们以GDFM为基础,进一步确立了一种新的观点,即延迟反馈带来的绩效差距可归因于时间差距和抽样差距。我们根据我们的分析,建议用时间距离和抽样复杂性的组合来衡量点击后信息的质量。培训目标将相应进行重新加权,以突出信息性和及时的信号。我们验证了对公共数据集的分析,实验性业绩证实了我们的方法的有效性。