MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 04. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Gong, Zhen [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XCH04276047X |
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| 245 | 1 | 0 | |a MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising |
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| 520 | |a Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users. However, multi-slot display advertising leads to different cost-effectiveness. Advertisers have the incentive to adjust bid prices so as to win the most economical ad positions. In this study, we introduce bid shading into multi-slot display advertising for bid price adjustment with a Multi-task End-to-end Bid Shading(MEBS) method. We prove the optimality of our method theoretically and examine its performance experimentally. Through extensive offline and online experiments, we demonstrate the effectiveness and efficiency of our method, and we obtain a 7.01% lift in Gross Merchandise Volume, a 7.42% lift in Return on Investment, and a 3.26% lift in ad buy count. | ||
| 650 | 4 | |a Computer Science - Computer Science and Game Theory |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Artificial Intelligence |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Niu, Lvyin |4 aut | |
| 700 | 1 | |a Zhao, Yang |4 aut | |
| 700 | 1 | |a Xu, Miao |4 aut | |
| 700 | 1 | |a Zheng, Zhenzhe |4 aut | |
| 700 | 1 | |a Zhang, Haoqi |4 aut | |
| 700 | 1 | |a Zhang, Zhilin |4 aut | |
| 700 | 1 | |a Wu, Fan |4 aut | |
| 700 | 1 | |a Bai, Rongquan |4 aut | |
| 700 | 1 | |a Yu, Chuan |4 aut | |
| 700 | 1 | |a Xu, Jian |4 aut | |
| 700 | 1 | |a Zheng, Bo |4 aut | |
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