858 citations
- University of Chinese Academy of SciencesCN44 papers
- Peking UniversityCN42 papers
- Shanghai Artificial Intelligence LaboratoryCN40 papers
- Chinese Academy of SciencesCN37 papers
- Tsinghua UniversityCN34 papers
- Institute of AutomationCN31 papers
- Shandong Institute of AutomationCN25 papers
- Beihang UniversityCN16 papers
- Shanghai Jiao Tong UniversityCN14 papers
- Chinese University of Hong KongHK12 papers
- Beijing Institute for General Artificial IntelligenceCN11 papers
- University of Hong KongHK11 papers
6 papers · 1 filter
GenCI: Generative Modeling of User Interest Shift via Cohort-based Intent Learning for CTR Prediction
Kesha Ou, Zhen Tian, Wayne Xin Zhao +2
Click-through rate (CTR) prediction plays a pivotal role in online advertising and recommender systems. Despite notable progress in modeling user preferences from historical behavi…
WebANNS: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers
Mugeng Liu, Siqi Zhong, Qi Yang +3
Approximate nearest neighbor search (ANNS) has become vital to modern AI infrastructure, particularly in retrieval-augmented generation (RAG) applications. Numerous in-browser ANNS…
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
Zepeng Huai, Yuji Yang, Mengdi Zhang +3
Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products c…
Modeling Two-Way Selection Preference for Person-Job Fit
Chen Yang, Yupeng Hou, Yang Song +3
Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers.…
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
Zepeng Huai, Zhe Wang, Yifan Zhu +1
Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword. This is a recommendation task…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…