AI 中文总结
针对工业级CVR预测中异构特征交互与多域序列建模未充分统一的问题，提出MaskRec拓扑掩码统一架构，在腾讯广告数据集上实现稳定性能提升。
AI 中文摘要
大规模点击后转化率（CVR）预测需要联合建模异构特征交互与多域用户行为序列的依赖关系。现有工业排序模型通常用独立模块处理这两方面，近期的统一架构尝试将二者整合到单一框架中，但这种统一往往依赖模块间协调，未能在同一交互空间内充分组织所有信息源。为解决该问题，本文提出MaskRec，一种用于特征交互与多域序列建模的拓扑掩码统一 token 交互架构。MaskRec 将异构特征、多域行为序列及上下文信号转换为统一的 token 表示，并引入可学习的全局记忆 token 与域级记忆 token 作为信息聚合节点。基于该统一 token 空间，MaskRec 设计了结构化注意力掩码 TopoMask，其根据不同信息源的结构差异与建模需求选择性启用或阻断注意力连接，使异构特征交互与多域序列建模在同一拓扑约束的注意力过程中完成。此外，MaskRec 整合了双路径交互式查询生成模块，在统一骨干网络前注入候选条件下的用户-物品交互信号。在腾讯广告算法大赛数据集上的实验表明，MaskRec 较官方基线取得稳定提升，验证了所提出的统一框架在工业级 CVR 预测中的有效性。
英文摘要
Large-scale post-click conversion rate (CVR) prediction requires jointly modeling heterogeneous feature interactions and dependencies over multi-domain user behavior sequences. Existing industrial ranking models usually handle these two aspects with separate modules. Recent unified architectures attempt to incorporate them into a single framework, but such unification often relies on coordination between modules and does not fully organize all information sources within the same interaction space. To address this problem, we propose MaskRec, a topology-masked unified token interaction architecture for feature interaction and multi-domain sequence modeling. MaskRec transforms heterogeneous features, multi-domain behavior sequences, and contextual signals into unified token representations, and further introduces learnable global memory tokens and domain-level memory tokens as information aggregation nodes. Based on this unified token space, MaskRec designs a structured attention mask, TopoMask, which selectively enables or blocks attention connections according to the structural differences and modeling requirements of different information sources. In this way, heterogeneous feature interaction and multi-domain sequence modeling are performed within the same topology-constrained attention process. In addition, MaskRec incorporates a dual-path interactive query generation module to inject candidate-conditioned user--item interaction signals before the unified backbone. Experiments on the Tencent Advertising Algorithm Competition dataset show that MaskRec achieves stable improvements over the official baseline, validating the effectiveness of the proposed unified framework for industrial CVR prediction.
CommentsAccepted to the TAAC-KDD Cup 2026 Workshop. Recipient of the Unified Block Innovation Award
