Deep Automated Mechanism for Integrating Ad Auction and Allocation in E-commerce Feeds

Core Concepts

This paper proposes a deep automated mechanism that integrates ad auction and allocation, ensuring both incentive compatibility (IC) and individual rationality (IR) in the presence of externalities while maximizing revenue and gross merchandise volume (GMV) for e-commerce platforms.

Abstract

The paper addresses the limitations of the prevalent methods of segregating ad auction and allocation into two distinct stages. These methods face two key problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement.
To solve these problems, the paper proposes a deep automated mechanism, called MIAA, that integrates ad auction and allocation. MIAA consists of three modules:

Externality-aware Prediction Module (EPM): This module takes a candidate allocation as input and outputs the predicted CTR and GMV for each item using a list-wise prediction model to capture global externalities.

Automated Auction Module (AAM): This module selects the optimal allocation by modeling the mechanism parameters 𝜇 and 𝜆 as deep neural networks, ensuring IC and IR properties.

Differentiable Sorting Module (DSM): This module uses a continuous relaxation of the sorting operation to enable end-to-end learning of the mechanism.

The proposed MIAA mechanism simultaneously decides the ranking, payment, and display position of the ads, while maximizing the platform's revenue and GMV. Offline experiments on public and industrial datasets, as well as online A/B tests on the Meituan retail delivery platform, demonstrate that MIAA outperforms state-of-the-art baselines in terms of revenue and GMV.

Stats

The paper reports the following key statistics:
On the Avito dataset:

The list-wise model in EPM improves the AUC by 0.0036 and the PCOC is closer to 1 compared to the point-wise model.
MIAA achieves a 5.35% improvement in Revenue+αGMV over the GSP and Fixed Position baseline.
On the Meituan dataset:

The list-wise model in EPM significantly improves the AUC from 0.6485 of the point-wise model to 0.7077.
MIAA achieves an 11.05% improvement in Revenue+αGMV over the GSP and Fixed Position baseline.

Quotes

None

Key Insights Distilled From

Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed

by Xuejian Li,Z... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2401.01656.pdf

Deeper Inquiries

How can the proposed mechanism be extended to handle more complex scenarios, such as multiple ad slots or different types of organic items (e.g., products, content)

The proposed mechanism can be extended to handle more complex scenarios by adapting the input and output structures to accommodate multiple ad slots and different types of organic items.
For multiple ad slots, the mechanism can be modified to consider a larger set of candidate ads and generate multiple candidate allocations for each ad slot. This would involve adjusting the prediction models to output the predicted results for each ad in each slot, taking into account the interactions between ads and their positions. The automated auction module would then need to select the optimal combination of ads for each slot based on the predicted performance metrics.
In the case of different types of organic items, the mechanism can incorporate additional features and attributes specific to each type of item. This would require enhancing the prediction models in the Externality-aware Prediction Module to capture the unique characteristics and impact of each type of organic item on ad performance. The Differentiable Sorting Module would need to consider the diversity of organic items when determining the optimal allocation.
By adapting the mechanism to handle these more complex scenarios, it can provide more tailored and effective ad placements, maximizing revenue and GMV across different ad slots and organic item types.

How can the mechanism be further improved to better balance the platform's revenue and GMV objectives, especially in cases where they may be conflicting

To better balance the platform's revenue and GMV objectives, especially in cases where they may be conflicting, the mechanism can be further improved in several ways:

Dynamic Weighting: Introduce dynamic weighting factors that can be adjusted based on real-time performance data. By monitoring the revenue and GMV outcomes of the mechanism, the weights assigned to each objective can be dynamically optimized to achieve the desired balance.

Constraint Optimization: Incorporate constraints or penalties in the optimization process to ensure that the mechanism does not prioritize one objective over the other excessively. By setting constraints on revenue and GMV targets, the mechanism can be guided to find a more balanced solution.

Multi-Objective Optimization: Implement multi-objective optimization techniques that explicitly consider both revenue and GMV as separate objectives. This approach would allow the mechanism to explore trade-offs between the two objectives and find optimal solutions that maximize both simultaneously.

By implementing these enhancements, the mechanism can achieve a more nuanced and adaptive approach to balancing revenue and GMV, ensuring that both objectives are optimized effectively.

What are the potential implications of the proposed mechanism on user experience and long-term platform sustainability

The proposed mechanism can have significant implications on user experience and long-term platform sustainability:

Improved Relevance: By integrating ad auction and allocation in a more holistic way, the mechanism can enhance the relevance and quality of ads displayed to users. This can lead to a more engaging and personalized user experience, increasing user satisfaction and interaction with the platform.

Optimized Revenue: The mechanism's focus on maximizing revenue and GMV can lead to a more sustainable business model for the platform. By ensuring that ads are placed strategically and efficiently, the platform can generate higher revenue while maintaining a positive user experience.

Long-Term Sustainability: The mechanism's ability to adapt to changing market conditions and user preferences can contribute to the long-term sustainability of the platform. By continuously optimizing ad placements and allocations, the platform can stay competitive and relevant in the evolving digital advertising landscape.

Overall, the proposed mechanism has the potential to drive positive outcomes for both users and the platform, fostering a balanced and sustainable ecosystem for all stakeholders involved.