aiqbench analysis
QueryFormer introduces a novel unified architecture that effectively bridges non-sequential features and sequential user behaviors for post-click conversion rate prediction, securing first place in the KDD Cup 2026 Tencent UniRec Challenge. The model leverages cross-attention mechanisms to generate query tokens, demonstrating superior scalability and efficiency compared to existing baselines like HyFormer while maintaining low inference latency. This advancement highlights significant progress in handling complex feature interactions within large-scale recommendation systems.
QueryFormer: Winning Solution for KDD Cup 2026 Tencent UniRec Challenge
arXiv:2609.16548v1 Announce Type: new Abstract: Post-click conversion rate (pCVR) prediction requires jointly modeling feature interactions and sequential user behaviors. The KDD Cup 2026 Tencent UniRec Challenge calls for a unified architecture addressing both. We observe that existing unified architectures often generate query tokens---the central information hub---with projection-based multi-layer perceptrons (MLPs), without explicit token-to-query attention for refining the query side. We propose QueryFormer, centered on a stackable unified field--sequence block that bridges non-sequential multi-field features and behavioral sequences, and provide a latency-aware scaling study over view width $H$, model width, depth, data, and compute. The block generates queries through cross-attention and packs sequence queries into shared-parameter attention. QueryFormer secured 1st place in the Industrial Track, achieving an official test area under the ROC curve (AUC) of 0.83254; a modest post-competition scale-up reached 0.832713. Within our grid, $H$-scaling improves validation AUC from 0.84540 to 0.84615 and beats HyFormer at comparable budgets. Ablation identifies query generation as the largest contributor. Packed shared-parameter cross-attention keeps H=8 inference latency to only 1.89x that of H=1, positioning the bridge as an efficient stackable unified block.
Abstract:Post-click conversion rate (pCVR) prediction requires jointly modeling feature interactions and sequential user behaviors. The KDD Cup 2026 Tencent UniRec Challenge calls for a unified architecture addressing both. We observe that existing unified architectures often generate query tokens---the central information hub---with projection-based multi-layer perceptrons (MLPs), without explicit token-to-query attention for refining the query side. We propose QueryFormer, centered on a stackable unified field--sequence block that bridges non-sequential multi-field features and behavioral sequences, and provide a latency-aware scaling study over view width $H$, model width, depth, data, and compute. The block generates queries through cross-attention and packs sequence queries into shared-parameter attention. QueryFormer secured 1st place in the Industrial Track, achieving an official test area under the ROC curve (AUC) of 0.83254; a modest post-competition scale-up reached 0.832713. Within our grid, $H$-scaling improves validation AUC from 0.84540 to 0.84615 and beats HyFormer at comparable budgets. Ablation identifies query generation as the largest contributor. Packed shared-parameter cross-attention keeps H=8 inference latency to only 1.89x that of H=1, positioning the bridge as an efficient stackable unified block.
Artificial Intelligence (cs.AI)
arXiv:2609.16548 [cs.AI]
(or arXiv:2609.16548v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.16548 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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