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- Tsinghua UniversityCN130 papers
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11 papers · 2 filters
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Chong Liu, Xiaoyang Liu, Rongqin Zheng +6
Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and…
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Chenglin Li, Mingjun Zhao, Huanming Zhang +5
Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to g…
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Jing Yao, Zhicheng Dou, Ruobing Xie +3
Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right t…
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
Zhenhui Xu, Meng Zhao, Liqun Liu +3
In industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. Deep learning is widely…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
Zijing Ou, Qinliang Su, Jianxing Yu +5
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval. To generate high-quality ha…