Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.01628

Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search

Greg Kocher·Sanjana Arun
View original source ↗Read source document ↗Export metadata ↓

Abstract

E-commerce search ranking must balance multiple objectives--relevance, user engagement, and platform revenue--when allocating impression slots to competing listings. Estimating the expected revenue component is well understood for fixed-price items, but becomes challenging when marketplace inventory includes mixed listing formats such as pure auctions and hybrid "Auction with Buy It Now" (ABIN) items, where prices evolve dynamically and the final transaction value is unknown at ranking time. Yet auction and ABIN listings account for a meaningful share of inventory and transaction volume on platforms such as eBay, and are a popular format for individual sellers and for unique items with unclear value. We extend the standard Expected Cost-per-Mille (eCPM) framework to auction and ABIN listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing one more impression of an item whose price is still evolving. The resulting formulation extends the familiar fixed-price eCPM--which is already inherently marginal--to auction dynamics, allowing unified ranking of fixed-price, auction, and ABIN listings under a single objective. We then describe a practical production implementation that approximates this objective, addressing cold-start challenges by bootstrapping from existing engagement models. Online A/B tests at a large e-commerce platform showed positive revenue gains and statistically significant improvements to user metrics, and the system was deployed to production.

Explore related subjects

cs.IRcs.LG

Keep this discovery

BibTeXRIS

Greg Kocher, Sanjana Arun. 2026-08-08. Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search. https://arxiv.org/abs/2609.01628

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.IR subjectcs.LG subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery

This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a Deep Multi-scale Aware Overcomplete (DeepMAO) inspired architecture is proposed that reconstructs clean IR images from emitter populated input, improving both image quality and downstream robotic performance. This approach outperforms existing enhancement techniques and enables reliable operation of vision driven robotic systems across illumination conditions from well-lit to extreme low-light scenes. The results outline the ability of this work to be able to mimic RGB styling from the scene and its applicability on robotics tasks that were trained on RGB images, opening the possibility of doing these tasks in extreme low-light without on-board lighting.

cs.RO↗
arXiv2026

Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles

Recommender systems (RS) greatly influence users' consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when training data only contain rating matrix, but they lack comprehensive solutions for scenarios where side features are present and utilized by the recommender. To address this gap, we extend the Leg-UP framework by enhancing the generator architecture to incorporate side features, enabling the generation of side-feature-aware fake user profiles. Experiments on benchmarks show that our method achieves strong attack performance while maintaining stealthiness.

cs.IR↗
arXiv2026

Nepali Passport Question Answering: A Low-Resource Dataset for Public Service Applications

Nepali, a low-resource language, faces significant challenges in building an effective information retrieval system due to the unavailability of annotated data and computational linguistic resources. In this study, we attempt to address this gap by preparing a pair-structured Nepali Question-Answer dataset. We focus on Frequently Asked Questions (FAQs) for passport-related services, building a data set for training and evaluation of IR models. In our study, we have fine-tuned transformer-based embedding models for semantic similarity in question-answer retrieval. The fine-tuned models were compared with the baseline BM25. In addition, we implement a hybrid retrieval approach, integrating fine-tuned models with BM25, and evaluate the performance of the hybrid retrieval. Our results show that the fine-tuned SBERT-based models outperform BM25, whereas multilingual E5 embedding-based models achieve the highest retrieval performance among all evaluated models.

cs.IR↗