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Shuokai Li
Researcher at Chinese Academy of Sciences
Publications - 8
Citations - 161
Shuokai Li is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Perplexity. The author has an hindex of 2, co-authored 4 publications receiving 69 citations.
Papers
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Proceedings ArticleDOI
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
TL;DR: Experimental results showed that Meta-Embedding can significantly improve both the cold-start and warm-up performances for six existing CTR prediction models, ranging from lightweight models such as Factorization Machines to complicated deep modelssuch as PNN and DeepFM.
Posted Content
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
TL;DR: Meta-Embedding as discussed by the authors is a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs by making use of previously learned ads through gradient-based meta learning.
Proceedings ArticleDOI
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
TL;DR: This paper proposes an entity debiasing framework (ENDEF) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective and demonstrates that the proposed framework can largely improve the performance of base fake news detectors, and online tests verify its superiority in practice.
Journal ArticleDOI
Learning policy scheduling for text augmentation.
TL;DR: The authors designed a search space over augmentation policies by integrating several common augmentation operations, and adopted a population-based training method to search the best augmentation schedule for text classification and machine translation tasks.
Journal ArticleDOI
Self-Supervised learning for Conversational Recommendation
TL;DR: Li et al. as mentioned in this paper explored the intrinsic correlations of different types of knowledge by self-supervised learning, and proposed the model SSCR, which stands for Self-Supervised learning for Conversational Recommendation.