Personalized Inter-Task Contrastive Learning for CTR&CVR Joint Estimation

In most of advertising and recommendation systems, multi-task learning (MTL) paradigm is widely employed to model diverse user behaviors (e.g., click, view, and purchase). Existing MTL models typically use task-shared networks with shared parameters or a routing mechanism to learn the commonalities between tasks while applying task-specific networks to learn the unique characteristics of each task. However, the potential relevance within task-specific networks is ignored, which is intuitively crucial for overall performance. In light of the fact that relevance is both task-complex and instance-specific, we present a novel learning paradigm to address these issues. In this paper, we propose Personalized Inter-task COntrastive Learning (PICO) framework, which can effectively model the inter-task relationship and is utilized to jointly estimate the click-through rate (CTR) and post-click conversion rate (CVR) in advertising systems. PICO utilizes contrastive learning to integrate inter-task knowledge implicitly from the task representations in task-specific networks. In addition, we introduce an auxiliary network to capture the inter-task relevance at instance-level and transform it into personalized temperature parameters for contrastive learning. With this method, fine-grained knowledge can be transferred to improve MTL performance without incurring additional inference costs. Both offline and online experiments show that PICO outperforms previous multi-task models significantly.