1.5k citations · 6.3k across the 95 of their papers we have counts for
9 papers · 1 filter
GDPNet: Refining Latent Multi-View Graph for Relation Extraction
Fuzhao Xue, Aixin Sun, Hao Zhang +1
Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is c…
An Embarrassingly Simple Model for Dialogue Relation Extraction
Fuzhao Xue, Aixin Sun, Hao Zhang +2
Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we propose a simple yet effective model named SimpleRE for…
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Xiaocong Chen, Lina Yao, Aixin Sun +3
Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation…
A Critical Study on Data Leakage in Recommender System Offline Evaluation
Yitong Ji, Aixin Sun, Jie Zhang +1
Recommender models are hard to evaluate, particularly under offline setting. In this paper, we provide a comprehensive and critical analysis of the data leakage issue in recommende…
A Re-visit of the Popularity Baseline in Recommender Systems
Yitong Ji, Aixin Sun, Jie Zhang +1
Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated…
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Cheng Zhao, Chenliang Li, Rong Xiao +2
In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interacti…