MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising

Основні поняття

The author introduces the MEBS method, applying bid shading to multi-slot display advertising. The approach optimizes shading ratio estimation to maximize expected surplus.

Анотація

MEBS introduces bid shading to multi-slot display advertising, optimizing shading ratio estimation through multi-task learning. Extensive experiments demonstrate its effectiveness and efficiency, leading to significant improvements in Gross Merchandise Volume, Return on Investment, and ad buy count.
Online advertising is crucial for Internet companies, with Real-Time Bidding enabling automated ad matching. Single-slot and multi-slot display advertising differ in cost-effectiveness due to varying user attention levels. Bid shading adjusts bid prices to win more economical positions.
Advertisers are incentivized to cut bid prices for lower but more cost-effective ad slots. Traditional bid shading methods focus on optimal bid price distribution but may not suit multi-slot display advertising due to changing ad positions affecting Click-Through Rate (CTR).
MEBS employs a multi-task end-to-end approach for bid shading in multi-slot display advertising. It optimizes the shading ratio based on win rate and calibrated predicted CTR, maximizing cost savings for advertisers.
Challenges in multi-slot display advertising include proving the optimality of bidding strategy with bid shading and addressing data sparsity issues in estimating optimal bid prices. MEBS addresses these challenges through an end-to-end paradigm and extensive offline and online experiments.

Статистика

We obtain a 7.01% lift in Gross Merchandise Volume.
A 7.42% lift in Return on Investment is achieved.
There is a 3.26% lift in ad buy count.

Цитати

Ключові висновки, отримані з

MEBS

by Zhen Gong,Lv... о arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02607.pdf

Глибші Запити

How does MEBS compare with traditional single-slot display advertising strategies

MEBS introduces bid shading into multi-slot display advertising, allowing advertisers to adjust bid prices strategically to win the most cost-effective ad positions. This approach differs from traditional single-slot display advertising strategies where there is only one slot for ad display. In single-slot advertising, the focus is on optimizing bids for a single position based on factors like click-through rates and conversion rates. MEBS, on the other hand, considers multiple slots in an auction and adjusts bid prices accordingly to maximize surplus.

What potential limitations or drawbacks could arise from implementing bid shading in multi-slot display advertising

Implementing bid shading in multi-slot display advertising may come with potential limitations or drawbacks:

Data Sparsity: Bid shading relies on estimating optimal bidding strategies based on historical data. In multi-slot advertising, where each slot has different levels of user attention and engagement, data sparsity can make it challenging to accurately predict outcomes.
Complexity: Managing bids across multiple slots adds complexity compared to single-slot advertising strategies. Advertisers need to consider various factors such as CTR variations across different positions.
Optimization Challenges: Finding the right balance between bid prices and ad positions in a multi-slot environment can be complex. Bid shading methods may need continuous optimization to adapt to changing market conditions.

How might advancements in machine learning impact the future of online advertising strategies

Advancements in machine learning are poised to revolutionize online advertising strategies by offering more sophisticated targeting capabilities, improved personalization, and enhanced efficiency:

Enhanced Targeting: Machine learning algorithms can analyze vast amounts of data to identify patterns and trends that help advertisers target specific audience segments more effectively.
Personalization: By leveraging machine learning models, advertisers can create personalized ad experiences tailored to individual users' preferences and behaviors.
Automation: Machine learning enables automated bidding processes that optimize campaigns in real-time based on performance metrics like conversions or ROI.
Predictive Analytics: Advanced machine learning techniques allow for predictive analytics that forecast future trends in consumer behavior, enabling proactive campaign adjustments.

Overall, advancements in machine learning are likely to lead towards more efficient and targeted online advertising strategies that deliver better results for advertisers while enhancing user experiences through relevant content delivery.