6 citations · 14 across the 12 of their papers we have counts for
8 papers · 1 filter
CausalRec: A CausalBoost Attention Model for Sequential Recommendation
Yunbo Hou, Tianle Yang, Ruijie Li +5
Recent advances in correlation-based sequential recommendation systems have demonstrated substantial success. Specifically, the attention-based model outperforms other RNN-based an…
FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning
Penghui Wei, Hongjian Dou, Shaoguo Liu +4
Conversion rate (CVR) estimation aims to predict the probability of conversion event after a user has clicked an ad. Typically, online publisher has user browsing interests and cli…
Gradient Coordination for Quantifying and Maximizing Knowledge Transference in Multi-Task Learning
Xuanhua Yang, Jianxin Zhao, Shaoguo Liu +2
Multi-task learning (MTL) has been widely applied in online advertising and recommender systems. To address the negative transfer issue, recent studies have proposed optimization m…
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking
Shanlei Mu, Penghui Wei, Wayne Xin Zhao +3
Multi-scenario ad ranking aims at leveraging the data from multiple domains or channels for training a unified ranking model to improve the performance at each individual scenario.…
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads
Penghui Wei, Yongqiang Chen, Shaoguo Liu +2
To increase brand awareness, many advertisers conclude contracts with advertising platforms to purchase traffic and then deliver advertisements to target audiences. In a whole deli…
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…