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This paper introduces MaskRec, a topology-masked unified token interaction architecture designed for joint feature interaction and multi-domain sequence modeling in post-click conversion rate (CVR) prediction. By transforming heterogeneous features and user behavior sequences into unified token representations and employing a structured attention mask, TopoMask, MaskRec effectively integrates diverse information sources within a single interaction space. Experimental results demonstrate that MaskRec significantly outperforms existing models, highlighting its potential for enhancing industrial CVR prediction accuracy.
MaskRec achieves a breakthrough in CVR prediction by integrating heterogeneous feature interactions and multi-domain sequences into a single, topology-constrained attention framework.
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.
