90 citations
- Chinese Academy of SciencesCN7 papers
- University of Chinese Academy of SciencesCN7 papers
- Meituan (China)CN5 papers
- University of Science and Technology of ChinaCN5 papers
- Institute of Computing TechnologyCN4 papers
- Beihang UniversityCN3 papers
- Beijing University of Posts and TelecommunicationsCN3 papers
- China Agricultural UniversityCN3 papers
- China Meat Research CentreCN3 papers
- Institute of SoftwareCN3 papers
- Nanyang Technological UniversitySG3 papers
- National University of SingaporeSG3 papers
8 papers · 1 filter
APP: Adaptive Prototypical Pseudo-Labeling for Few-shot OOD Detection
Pei Wang, Keqing He, Yutao Mou +6
Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection studies generally work on the assumption that plent…
SoccerNet 2023 Challenges Results
Anthony Cioppa, Silvio Giancola, Vladimir Somers +99
The SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vi…
NEON: Living Needs Prediction System in Meituan
Xiaochong Lan, Chen Gao, Shiqi Wen +6
Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect user…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
Huinan Sun, Guangliang Yu, Pengye Zhang +3
Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising syst…
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment
Xin Xin, Xiangyuan Liu, Hanbing Wang +8
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…