Search Results for author:
Found 4 papers, 3 papers with code
Customized Conversational Recommender Systems
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.
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Recommendation Systems
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Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings
We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs.
