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- Zhejiang UniversityCN57 papers
- Peking UniversityCN29 papers
- Alibaba Group (United States)US28 papers
- Tsinghua UniversityCN26 papers
- Shanghai Jiao Tong UniversityCN21 papers
- University of Science and Technology of ChinaCN18 papers
- Chinese Academy of SciencesCN15 papers
- Wuhan UniversityCN14 papers
- Nanyang Technological UniversitySG12 papers
- University of Chinese Academy of SciencesCN11 papers
- Hong Kong University of Science and TechnologyHK10 papers
- Huazhong University of Science and TechnologyCN10 papers
21 papers · 2 filters
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
Yuting Chen, Yanshi Wang, Yabo Ni +2
Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users…
Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang +6
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifical…
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
Zheng Gao, Hongsong Li, Zhuoren Jiang +1
Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in suc…
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Yufei Feng, Fuyu Lv, Binbin Hu +5
Click-through rate (CTR) prediction is a critical task for many industrial systems, such as display advertising and recommender systems. Recently, modeling user behavior sequences…
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Wentao Ouyang, Xiuwu Zhang, Lei Zhao +5
Click-through rate (CTR) prediction is a critical task in online advertising systems. Existing works mainly address the single-domain CTR prediction problem and model aspects such…
Learning Personalized Risk Preferences for Recommendation
Yingqiang Ge, Shuyuan Xu, Shuchang Liu +3
The rapid growth of e-commerce has made people accustomed to shopping online. Before making purchases on e-commerce websites, most consumers tend to rely on rating scores and revie…