Click-through rate (CTR) and post-click conversion rate (CVR) predictions are two fundamental modules in industrial ranking systems such as recommender systems, advertising, and search engines. Since CVR involves much fewer samples than CTR (known as the CVR data sparsity problem), most of the existing works try to leverage CTR&CVR multi-task learning to improve CVR performance. However, typical coarse-grained sub-network/layer sharing methods may introduce conflicts and lead to performance degradation, since not every neuron or neuron connection in one layer should be shared between CVR and CTR tasks. This is because users may have different fine-grained content feature preferences between deep consumption and click behavior, represented by CVR and CTR, respectively. To address this sharing&conflict problem, we propose a novel multi-task CVR modeling scheme with neuron-connection level sharing named NCS4CVR, which can automatically and flexibly learn which neuron weights are shared or not shared without artificial experience. Compared with previous layer-level sharing methods, this is the first time that a fine-grained CTR&CVR sharing method at the neuron connection level is proposed, which is a research paradigm shift in the sharing level. Both offline and online experiments demonstrate that our method outperforms both the single-task model and the layer-level sharing model. Our proposed method has now been successfully deployed in an industry video recommender system serving major traffic.

翻译：点击率（CTR）与点击后转化率（CVR）预测是推荐系统、广告及搜索引擎等工业排序系统中的两个核心模块。由于CVR涉及的样本量远少于CTR（即CVR数据稀疏性问题），现有研究多尝试利用CTR与CVR的多任务学习来提升CVR性能。然而，典型的粗粒度子网络/层共享方法可能引入冲突并导致性能下降，因为同一层中并非所有神经元或神经元连接都应在CVR与CTR任务间共享。其原因在于，用户对深度消费行为（由CVR表征）与点击行为（由CTR表征）可能具有不同的细粒度内容特征偏好。为解决这一共享与冲突问题，我们提出了一种新颖的、基于神经元连接级别共享的多任务CVR建模方案NCS4CVR，该方案能够无需人工经验、自动且灵活地学习哪些神经元权重应被共享或不被共享。与先前的层级共享方法相比，这是首次在神经元连接级别提出细粒度的CTR与CVR共享方法，代表了共享级别上的研究范式转变。离线与在线实验均表明，我们的方法优于单任务模型及层级共享模型。目前，所提方法已成功部署于服务主要流量的工业级视频推荐系统中。