# Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach

arXiv: https://arxiv.org/abs/2105.13556 · HTML: https://mlsanity.com/paper/2105.13556/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 23 references that are in the map.
- First posted on arXiv 28 May 2021.

## Built on by (most cited first, 5 of 5)

- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [Ads Supply Personalization via Doubly Robust Learning](https://mlsanity.com/paper/2410.12799/) (2024)
- [Session-Level Dynamic Ad Load Optimization using Offline Robust Reinforcement Learning](https://mlsanity.com/paper/2501.05591/) (2025)
- [Adaptive Ad Load Design for Sponsored Search Markets: Evidence, Theory, and Deployment](https://mlsanity.com/paper/2607.14418/) (2026)

## Abstract (arXiv)

In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior. The former content helps advertisers achieve their marketing goals and provides a stream of ad revenue to the platform. The latter content contributes to users' engagement with the platform, which is key to its long-term health. A burning issue for e-commerce platform design is how to blend advertising with content in a way that respects these interactions and balances these multiple business objectives. This paper describes a system developed for this purpose in the context of blending personalized sponsored content with non-sponsored content on the product detail pages of JD.COM, an e-commerce company. This system has three key features: (1) Optimization of multiple competing business objectives through a new virtual bids approach and the expressiveness of the latent, implicit valuation of the platform for the multiple objectives via these virtual bids. (2) Modeling of users' click behavior as a function of their characteristics, the individual characteristics of each sponsored content and the influence exerted by other sponsored and non-sponsored content displayed alongside through a deep learning approach; (3) Consideration of externalities in the allocation of ads, thereby making it directly compatible with a Vickrey-Clarke-Groves (VCG) auction scheme for the computation of payments in the presence of these externalities. The system is currently deployed and serving all traffic through JD.COM's mobile application. Experiments demonstrating the performance and advantages of the system are presented.