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Blending Advertising with Organic Content in E-commerce via Virtual Bids
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a data-tuned virtual bid to balance ad CTR and revenue | JD.COM listwise ad allocation · JD.COM product-detail pages | ad CTR: +2.05% | Helped |
| Use a data-tuned virtual bid to balance ad CTR and revenue | JD.COM listwise ad allocation · JD.COM product-detail pages | ad revenue: +9.25% | Helped |
| Use a manually selected CTR constraint C | JD.COM ad allocation · JD.COM product-detail pages | ad CTR: -4.79%** | Hurt |
| Use a manually selected CTR constraint C | JD.COM ad allocation · JD.COM product-detail pages | ad revenue: +13.57%** | Helped |
| Shuffle the ordering of the ads | baseline ad allocation · JD.COM product-detail pages | ad CTR: -1.44%** | Hurt |
| Randomly select and place ads from the top Kads+1 candidates | baseline ad allocation · JD.COM product-detail pages | ad CTR: -1.90%** | Hurt |
| Randomly select and place ads from the top Kads+2 candidates | baseline ad allocation · JD.COM product-detail pages | ad CTR: -4.07%** | Hurt |
| Use listwise CTR prediction that models interactions among ads and organic items rather than pointwise prediction | JD.COM CTR prediction · Offline validation | AUC: 0.65 -> 0.75 | Helped |
| Use listwise CTR prediction that models interactions among ads and organic items rather than pointwise prediction | JD.COM CTR prediction · Offline validation | F1 Score: 0.065 -> 0.08 | Helped |
| Keep the virtual bid tuned for the earlier period instead of retuning it | JD.COM listwise ad allocation · JD.COM product-detail pages, T2 vs T1 | ad revenue: -8.55%** | Hurt |
| Keep the manually tuned constraint C fixed across periods | JD.COM constrained ad allocation · JD.COM product-detail pages, T2 vs T1 | ad revenue: -9.33%** | Hurt |
| Consider joint effects when selecting ad tuples | JD.COM listwise ad allocation · JD.COM product-detail pages | ads with more than one subcategory: +6.06%** | Helped |
Closest papers by meaning
- On Designing the Optimal Integrated Ad Auction in E-commerce Platforms (AAAI 2025 · similarity 0.83)
- LLM-Auction: Generative Auction towards LLM-Native Advertising (NeurIPS 2026 · similarity 0.83)
- Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms (AAAI 2024 · similarity 0.83)
- Merging Mechanisms for Ads and Organic Items in E-commerce Platforms (AAAI 2025 · similarity 0.83)
- Truthful Aggregation of LLMs with an Application to Online Advertising (NeurIPS 2025 · similarity 0.82)
- Learning Personalized Ad Impact via Contextual Reinforcement Learning under Delayed Rewards (NeurIPS 2025 · similarity 0.82)
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