Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
Carlos Carrion, Zenan Wang, Harikesh Nair, Xianghong Luo, Yulin Lei,, Xiliang Lin, Wenlong Chen, Qiyu Hu, Changping Peng, Yongjun Bao, Weipeng, Yan

TL;DR
This paper presents a novel system for blending sponsored and non-sponsored content on e-commerce platforms, optimizing multiple objectives through virtual bids, deep learning user behavior models, and externality considerations, deployed at JD.COM.
Contribution
It introduces a virtual bids optimization approach that balances multiple business objectives and models user interactions with content using deep learning.
Findings
System successfully deployed at JD.COM with positive performance results.
Optimizes multiple objectives simultaneously with a new virtual bids method.
Effectively models user behavior and externalities in ad allocation.
Abstract
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…
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Taxonomy
TopicsConsumer Market Behavior and Pricing · Auction Theory and Applications · Recommender Systems and Techniques
