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Found 12 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.
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.
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.
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.
A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking
Our method consists of two modules, KG-based learning enhancement and multi-claim semantic composition.
Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction
Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document.
Ranked #1 on
Emotion-Cause Pair Extraction
on ECPE-FanSplit
Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
The bottom component of our framework classifies the stances of tweets in a conversation discussing a rumor via modeling the structural property based on a novel graph convolutional network.
