In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). While, existing MTL approaches are either rigid to adapt to different scenarios, or only capture coarse-grained task relatedness, thus making it difficult to effectively transfer knowledge across tasks. To address these issues, in this paper, we propose an Adaptive Fine-grained Task Relatedness modeling approach, AdaFTR, for joint CTR-CVR estimation. Our approach is developed based on a parameter-sharing MTL architecture, and introduces a novel adaptive inter-task representation alignment method based on contrastive learning.Given an instance, the inter-task representations of the same instance are considered as positive, while the representations of another random instance are considered as negative. Furthermore, we explicitly model fine-grained task relatedness as the contrast strength (i.e. the temperature coefficient in InfoNCE loss) at the instance level. For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance. In this way, we can adaptively set the temperature for contrastive learning in a fine-grained way (i.e. instance level), so as to better capture task relatedness. Both offline evaluation with public e-commerce datasets and online test in a real advertising system at Alibaba have demonstrated the effectiveness of our approach.

翻译：在现代广告与推荐系统中，多任务学习范式已被广泛用于联合预测多种用户反馈行为（如点击与购买）。然而，现有MTL方法要么难以灵活适配不同场景，要么仅能捕获粗粒度的任务相关性，导致跨任务知识迁移困难。针对这些问题，本文提出一种面向联合CTR-CVR估计的自适应细粒度任务相关性建模方法AdaFTR。该方法基于参数共享的MTL架构，创新性地引入基于对比学习的自适应跨任务表示对齐机制：对于给定样本，其跨任务表示视为正样本对，而其他随机样本的跨任务表示视为负样本对。此外，我们将细粒度任务相关性显式建模为实例级对比强度（即InfoNCE损失中的温度系数）。为此，我们构建一个相关性预测网络，使其能够预测样本跨任务表示的对比强度，从而以细粒度方式（即实例级）自适应设定对比学习温度参数，更精准地捕获任务相关性。基于公开电商数据集的离线评估与阿里巴巴真实广告系统的在线测试共同验证了本方法的有效性。