Click-through rate (CTR) prediction, which models behavior sequence and non-sequential features (e.g., user/item profiles or cross features) to infer user interest, underpins industrial recommender systems. However, most methods face three forms of heterogeneity that degrade predictive performance: (i) Feature Heterogeneity persists when limited sequence side features provide less granular interest representation compared to extensive non-sequential features, thereby impairing sequence modeling performance; (ii) Context Heterogeneity arises because a user's interest in an item will be influenced by other items, yet point-wise prediction neglects cross-item interaction context from the entire item set; (iii) Architecture Heterogeneity stems from the fragmented integration of specialized network modules, which compounds the model's effectiveness, efficiency and scalability in industrial deployments. To tackle the above limitations, we propose HoMer, a Homogeneous-Oriented TransforMer for modeling sequential and set-wise contexts. First, we align sequence side features with non-sequential features for accurate sequence modeling and fine-grained interest representation. Second, we shift the prediction paradigm from point-wise to set-wise, facilitating cross-item interaction in a highly parallel manner. Third, HoMer's unified encoder-decoder architecture achieves dual optimization through structural simplification and shared computation, ensuring computational efficiency while maintaining scalability with model size. Without arduous modification to the prediction pipeline, HoMer successfully scales up and outperforms our industrial baseline by 0.0099 in the AUC metric, and enhances online business metrics like CTR/RPM by 1.99%/2.46%. Additionally, HoMer saves 27% of GPU resources via preliminary engineering optimization, further validating its superiority and practicality.

翻译：点击率（CTR）预测通过建模行为序列与非序列特征（例如用户/物品画像或交叉特征）来推断用户兴趣，是工业推荐系统的基础。然而，大多数方法面临三种形式的异质性，导致预测性能下降：（i）特征异质性：当有限的序列侧特征相比丰富的非序列特征提供更粗糙的兴趣表示时，会损害序列建模性能；（ii）上下文异质性：用户对某物品的兴趣会受到其他物品的影响，但逐点预测忽略了整个物品集合中的跨物品交互上下文；（iii）架构异质性：源于专用网络模块的碎片化集成，这会损害模型在工业部署中的效果、效率与可扩展性。为应对上述局限，我们提出HoMer，一种面向同质化的Transformer，用于建模序列与集合上下文。首先，我们将序列侧特征与非序列特征对齐，以实现精确的序列建模和细粒度的兴趣表示。其次，我们将预测范式从逐点转向集合式，以高度并行的方式促进跨物品交互。第三，HoMer统一的编码器-解码器架构通过结构简化和计算共享实现双重优化，在保持模型规模可扩展性的同时确保计算效率。无需对预测流程进行复杂修改，HoMer成功实现规模化扩展，并在AUC指标上超越我们的工业基线0.0099，同时将在线业务指标如CTR/RPM提升了1.99%/2.46%。此外，通过初步的工程优化，HoMer节省了27%的GPU资源，进一步验证了其优越性与实用性。