Selected work

Representative Papers

Measuring Opportunity Cost with Stock Lifetime Value

Jul 02, 2026

This study addresses the limitation of conventional e-commerce A/B tests, which often overlook the long-term impact of interventions on profitability across an inventory item’s full lifecycle due to short experimental windows. To overcome this, the authors propose Stock Lifetime Value (SLV), a novel metric that aggregates the expected profit of current inventory over its entire sales horizon within short-term experiments, thereby enabling more accurate assessment of long-term profitability. SLV uniquely integrates inventory constraints and seasonal lifecycle dynamics into the A/B testing framework, combining causal inference with financial mapping to support both item-level and user-level experimentation while aligning with annual financial reporting. Empirical validation at Zalando demonstrates that SLV effectively predicts actual profits over an 18-month horizon, enhances pricing algorithm performance, and delivers interpretable estimates of annual financial impact.

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VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions

Jun 17, 2026

This work addresses the challenges of user interaction scarcity and positional/duration biases in e-commerce short videos under extreme cold-start scenarios by proposing the first zero-shot multimodal retrieval framework tailored for e-commerce video streams. Built upon a domain-adapted CLIP vision-language model, the framework aligns user intent and video content into a shared semantic space, enabling bidirectional product-to-video and semantic retrieval. It further provides a systematic comparison between discriminative (CLIP-based) and generative (LLM-based) embedding strategies to optimize candidate generation. Online A/B testing demonstrates that the proposed approach significantly improves deep video completion rates by 50%, validating its effectiveness and scalability in large-scale real-world e-commerce environments.

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High-Frequency Pricing at Scale for E-Commerce

Jun 11, 2026

This study addresses the challenges of extreme demand volatility, delayed pricing responses, and misalignment between short-term revenue and long-term profitability during major fashion e-commerce promotions. To tackle these issues, the authors propose a high-frequency “predict–optimize” automated pricing system that breaks away from traditional weekly decision cycles by operating at a minute-level granularity. The system achieves the first industrial-scale deployment of daily multi-objective dynamic pricing in large-scale e-commerce settings, combining gradient-boosted tree models for daily demand forecasting with a multi-objective optimization framework to generate real-time pricing strategies that jointly maximize long-term profit and net merchandise value. Evaluated across 23 A/B tests in 12 Zalando markets from 2023 to 2024, the system delivered approximately 6% higher profit while maintaining sales volume and has since been fully deployed for promotional pricing.

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InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement

Apr 21, 2026

The scarcity of large-scale, semantically plausible, and contextually consistent 3D human-scene interaction data hinders embodied agents’ understanding of 3D environments. This work proposes InHabit, the first framework to transfer knowledge from internet-scale 2D vision foundation models to the task of 3D human placement, establishing an end-to-end automated generation pipeline. It leverages a vision-language model to recommend contextually appropriate actions, employs an image editing model to synthesize humans, and refines their poses into SMPL-X representations aligned with scene geometry. Built upon Habitat-Matterport3D, the method produces a large-scale dataset comprising 78K samples across 800 building-scale scenes, significantly improving performance in RGB-based human reconstruction and contact estimation. In user studies, 78% of participants preferred its outputs over those of existing methods.

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Long-Term Embeddings for Balanced Personalization

Apr 09, 2026

This work addresses critical limitations in Transformer-based sequential recommendation, including severe recency bias, inadequate modeling of long-term user preferences, and online-offline inconsistency caused by single-version feature storage. To tackle these issues, the authors propose the High-Inertia Long-Term Embedding (LTE) framework, which enforces cross-version consistency by anchoring content representations to fixed semantic bases. LTE embeddings are integrated as prefix tokens into a causal language model via a lagged window, effectively balancing short- and long-term interest modeling. The approach innovatively combines an asymmetric autoencoder with a lagged ensemble strategy to prevent data leakage while enhancing personalization stability. Extensive A/B testing on the Zalando platform demonstrates significant improvements in user engagement and key business metrics.

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Recent publications

Latest Papers

Measuring Opportunity Cost with Stock Lifetime Value

Jul 02, 2026

This study addresses the limitation of conventional e-commerce A/B tests, which often overlook the long-term impact of interventions on profitability across an inventory item’s full lifecycle due to short experimental windows. To overcome this, the authors propose Stock Lifetime Value (SLV), a novel metric that aggregates the expected profit of current inventory over its entire sales horizon within short-term experiments, thereby enabling more accurate assessment of long-term profitability. SLV uniquely integrates inventory constraints and seasonal lifecycle dynamics into the A/B testing framework, combining causal inference with financial mapping to support both item-level and user-level experimentation while aligning with annual financial reporting. Empirical validation at Zalando demonstrates that SLV effectively predicts actual profits over an 18-month horizon, enhances pricing algorithm performance, and delivers interpretable estimates of annual financial impact.

0 citationsRead paper

VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions

Jun 17, 2026

This work addresses the challenges of user interaction scarcity and positional/duration biases in e-commerce short videos under extreme cold-start scenarios by proposing the first zero-shot multimodal retrieval framework tailored for e-commerce video streams. Built upon a domain-adapted CLIP vision-language model, the framework aligns user intent and video content into a shared semantic space, enabling bidirectional product-to-video and semantic retrieval. It further provides a systematic comparison between discriminative (CLIP-based) and generative (LLM-based) embedding strategies to optimize candidate generation. Online A/B testing demonstrates that the proposed approach significantly improves deep video completion rates by 50%, validating its effectiveness and scalability in large-scale real-world e-commerce environments.

0 citationsRead paper

High-Frequency Pricing at Scale for E-Commerce

Jun 11, 2026

This study addresses the challenges of extreme demand volatility, delayed pricing responses, and misalignment between short-term revenue and long-term profitability during major fashion e-commerce promotions. To tackle these issues, the authors propose a high-frequency “predict–optimize” automated pricing system that breaks away from traditional weekly decision cycles by operating at a minute-level granularity. The system achieves the first industrial-scale deployment of daily multi-objective dynamic pricing in large-scale e-commerce settings, combining gradient-boosted tree models for daily demand forecasting with a multi-objective optimization framework to generate real-time pricing strategies that jointly maximize long-term profit and net merchandise value. Evaluated across 23 A/B tests in 12 Zalando markets from 2023 to 2024, the system delivered approximately 6% higher profit while maintaining sales volume and has since been fully deployed for promotional pricing.

0 citationsRead paper

InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement

Apr 21, 2026

The scarcity of large-scale, semantically plausible, and contextually consistent 3D human-scene interaction data hinders embodied agents’ understanding of 3D environments. This work proposes InHabit, the first framework to transfer knowledge from internet-scale 2D vision foundation models to the task of 3D human placement, establishing an end-to-end automated generation pipeline. It leverages a vision-language model to recommend contextually appropriate actions, employs an image editing model to synthesize humans, and refines their poses into SMPL-X representations aligned with scene geometry. Built upon Habitat-Matterport3D, the method produces a large-scale dataset comprising 78K samples across 800 building-scale scenes, significantly improving performance in RGB-based human reconstruction and contact estimation. In user studies, 78% of participants preferred its outputs over those of existing methods.

0 citationsRead paper

Long-Term Embeddings for Balanced Personalization

Apr 09, 2026

This work addresses critical limitations in Transformer-based sequential recommendation, including severe recency bias, inadequate modeling of long-term user preferences, and online-offline inconsistency caused by single-version feature storage. To tackle these issues, the authors propose the High-Inertia Long-Term Embedding (LTE) framework, which enforces cross-version consistency by anchoring content representations to fixed semantic bases. LTE embeddings are integrated as prefix tokens into a causal language model via a lagged window, effectively balancing short- and long-term interest modeling. The approach innovatively combines an asymmetric autoencoder with a lagged ensemble strategy to prevent data leakage while enhancing personalization stability. Extensive A/B testing on the Zalando platform demonstrates significant improvements in user engagement and key business metrics.

0 citationsRead paper