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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.
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).
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