Effective Knowledge Transfer for Multi-Task Recommendation Models

2026-05-07

AI Summary

This work addresses the challenge of conversion rate (CVR) prediction, which is hindered by extreme data sparsity in user behavior. To overcome this, the authors propose a novel multi-task knowledge transfer architecture featuring a router module that dynamically allocates cross-task knowledge, a receiver module that selectively captures and transforms relevant information, and an enhancement module designed to ensure transferred knowledge positively contributes to the target task. This framework enables efficient, controllable, and beneficial knowledge sharing across tasks. Extensive experiments on multiple public benchmarks demonstrate significant performance gains over state-of-the-art methods. Furthermore, online A/B tests show a 3.93% increase in eCPM, and the approach has been successfully deployed in two major industrial recommendation scenarios.

Abstract

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.

Problem

• Conversion Rate Prediction
• Multi-Task Recommendation
• Data Sparsity
• Knowledge Transfer
• User Behavior Modeling

Innovation

• Knowledge Transfer
• Multi-task Learning
• Recommendation Systems
• Conversion Rate Prediction
• Task-specific Routing