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Found 16 papers, 3 papers with code
MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising
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.
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding
The trained policy can subsequently be deployed for further data collection, resulting in an iterative training framework, which we refer to as iterative offline RL.
Scalable Virtual Valuations Combinatorial Auction Design by Combining Zeroth-Order and First-Order Optimization Method
Subsequently, we propose a novel optimization method that combines both zeroth-order and first-order techniques to optimize the VVCA parameters.
Sustainable Online Reinforcement Learning for Auto-bidding
Due to safety concerns, it was believed that the RL training process can only be carried out in an offline virtual advertising system (VAS) that is built based on the historical data generated in the RAS.
Hierarchically Constrained Adaptive Ad Exposure in Feeds
To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed.
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
In previous work, researchers have developed several calibration methods to post-process the outputs of a predictor to obtain calibrated values, such as binning and scaling methods.
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
Computation Resource Allocation Solution in Recommender Systems
On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.
High harmonics from backscattering of delocalized electrons
It is shown that electron backscattering can enhance high-harmonic generation in periodic systems with broken translational symmetry.
Atomic Physics Mesoscale and Nanoscale Physics
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Learning Adaptive Display Exposure for Real-Time Advertising
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
