The conversion rate (CVR) is a crucial metric for evaluating the effectiveness of platforms, as it quantifies the alignment of content with audience preferences. However, the limited nature of customers' conversion actions presents a significant challenge for training ranking models effectively. In this paper, we propose an Effective Knowledge Transfer method for Multi-task Recommendation Models (EKTM). This method enables the ranking model to learn from diverse user behaviors, thereby enhancing performance through the transfer of knowledge across distinct yet related tasks. Each specific CVR task can directly benefit from the insights provided by other tasks. To achieve this, we first introduce a router module that integrates and disseminates knowledge across tasks. Subsequently, each CVR task is equipped with a transmitter module that facilitates the transformation of knowledge from the router. Additionally, we propose an enhanced module to ensure that the transferred knowledge benefit the original task learning. Extensive experiments on several benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches. Online A/B testing on a commercial platform has validated the effectiveness of the EKTM algorithm in large-scale industrial settings, resulting in a 3.93% uplift in effective Cost Per Mille (eCPM). The algorithm has since been fully deployed across two of the platform's main-traffic scenarios.

翻译：转化率（CVR）是衡量平台效果的关键指标，因为它量化了内容与用户偏好的匹配程度。然而，用户转化行为的稀疏性对有效训练排序模型构成了重大挑战。本文提出了一种面向多任务推荐模型的高效知识迁移方法（EKTM）。该方法使排序模型能够从多样化的用户行为中学习，通过跨不同但相关任务的知识迁移来提升性能，每个具体CVR任务都能直接受益于其他任务提供的洞见。为此，我们首先引入一个路由模块，用于整合并分发跨任务的知识；随后为每个CVR任务配备一个转换模块，以促进来自路由器的知识转化；此外，我们还提出一个增强模块，确保迁移的知识有助于原始任务的学习。在多个基准数据集上的大量实验表明，所提方法优于现有最先进方法。某商业平台的在线A/B测试验证了EKTM算法在工业级规模下的有效性，有效千次展示成本（eCPM）提升了3.93%。该算法现已全面部署于该平台的两个主要流量场景中。