In this paper, we introduce Star+, a novel multi-domain model for click-through rate (CTR) prediction inspired by the Star model. Traditional single-domain approaches and existing multi-task learning techniques face challenges in multi-domain environments due to their inability to capture domain-specific data distributions and complex inter-domain relationships. Star+ addresses these limitations by enhancing the interaction between shared and domain-specific information through various fusion strategies, such as add, adaptive add, concatenation, and gating fusions, to find the optimal balance between domain-specific and shared information. We also investigate the impact of different normalization techniques, including layer normalization, batch normalization, and partition normalization, on the performance of our model. Our extensive experiments on both industrial and public datasets demonstrate that Star+ significantly improves prediction accuracy and efficiency. This work contributes to the advancement of recommendation systems by providing a robust, scalable, and adaptive solution for multi-domain environments.

翻译：本文介绍了Star+，一种受Star模型启发的新型多领域点击率预测模型。传统的单领域方法和现有的多任务学习技术在多领域环境中面临挑战，因为它们无法捕捉领域特定的数据分布和复杂的领域间关系。Star+通过增强共享信息与领域特定信息之间的交互来解决这些限制，采用了多种融合策略，如加法、自适应加法、拼接和门控融合，以找到领域特定信息与共享信息之间的最佳平衡。我们还研究了不同归一化技术（包括层归一化、批归一化和分区归一化）对我们模型性能的影响。我们在工业数据集和公共数据集上进行的大量实验表明，Star+显著提高了预测准确性和效率。这项工作通过为多领域环境提供一个鲁棒、可扩展且自适应的解决方案，推动了推荐系统的发展。