DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

• URL: http://arxiv.org/abs/2606.10243v1
• Date: Mon, 08 Jun 2026 23:13:58 GMT
• Title: DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
• Abstract summary: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.<n>This task presents a modeling challenge: click signals are abundant and exhibit short temporal horizons, whereas conversion signals are inherently sparse, long-delayed, and frequently unattributed.<n>We propose DUET, a framework that explicitly partitions user behavioral data into two domain-coherent streams -- clicks and conversions.
• Score: 8.972672149050627
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems. This task presents a modeling challenge: click signals are abundant and exhibit short temporal horizons, whereas conversion signals are inherently sparse, long-delayed, and frequently unattributed. Despite these statistical disparities, both signal types must inform models that operate within strict serving-latency constraints. Prior pre-training approaches address this heterogeneity with a single, undifferentiated encoder applied uniformly across both data streams. We propose DUET (Dual User Embedding Transformers), a framework that explicitly partitions user behavioral data into two domain-coherent streams -- clicks and conversions -- and pre-trains dedicated transformer encoders with architectures tailored to each stream's statistical characteristics: multi-layer self-attention for the dense click stream and interleaved cross- and self-attention for the sparse conversion stream. The resulting complementary embeddings are jointly consumed by a downstream ranker without exceeding serving-latency budgets. Evaluation demonstrates up to 0.38% normalized entropy (NE) reduction relative to the strongest baseline, and A/B test shows consistent improvements in OCVR prediction accuracy.

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