2 citations · 5 across the 15 of their papers we have counts for
8 papers · 1 filter
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
Zixuan Xu, Penghui Wei, Shaoguo Liu +3
Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse de…
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation
Zihan Lin, Xuanhua Yang, Xiaoyu Peng +4
In modern advertising and recommender systems, multi-task learning (MTL) paradigm has been widely employed to jointly predict diverse user feedbacks (e.g. click and purchase). Whil…
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Xuanhua Yang, Xiaoyu Peng, Penghui Wei +3
Click-through rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have proved that learning a unified model to serve multiple…
MCMF: Multi-Constraints With Merging Features Bid Optimization in Online Display Advertising
Xiao Wang, Shaoguo Liu, Yidong Jia +4
In the Real-Time Bidding (RTB), advertisers are increasingly relying on bid optimization to gain more conversions (i.e trade or arrival). Currently, the efficiency of bid optimizat…
Towards Personalized Bundle Creative Generation with Contrastive Non-Autoregressive Decoding
Penghui Wei, Shaoguo Liu, Xuanhua Yang +2
Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle cr…
CREATER: CTR-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning
Penghui Wei, Xuanhua Yang, Shaoguo Liu +2
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). W…
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
Penghui Wei, Weimin Zhang, Ruijie Hou +4
Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likeliho…
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Zixuan Xu, Penghui Wei, Weimin Zhang +3
In online advertising, conventional post-click conversion rate (CVR) estimation models are trained using clicked samples. However, during online serving the models need to estimate…