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Found 13 papers, 1 papers with code
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
We introduce FedAds, the first benchmark for CVR estimation with vFL, to facilitate standardized and systematical evaluations for vFL algorithms.
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems.
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads
In a whole delivery period, advertisers usually desire a certain impression count for the ads, and they also expect that the delivery performance is as good as possible (e. g., obtaining high click-through rate).
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking.
Global Balanced Experts for Federated Long-Tailed Learning
In particular, a proxy is derived from the accumulated gradients uploaded by the clients after local training, and is shared by all clients as the class prior for re-balance training.
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains.
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems.
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
Current bundle generation studies focus on generating a combination of items to improve user experience.
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR).
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
Calibration techniques aims to post-process model predictions to posterior probabilities.
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels.
Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach
In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted.
