activity
20202022
most citedCausPref: Causal Preference Learning for Out-of-Distribution Recommendation

49 citations · 60 across the 10 of their papers we have counts for

collaborators

12 papers

cs.LG202249 cited

CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

Yue He, Zimu Wang, Peng Cui +4

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to…

cs.CV2021

Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation

Ke Zhang, Sihong Chen, Qi Ju +3

With the increase in the number of image data and the lack of corresponding labels, weakly supervised learning has drawn a lot of attention recently in computer vision tasks, espec…

cs.CL2021

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations

Xinyin Ma, Yong Jiang, Nguyen Bach +4

Entity retrieval, which aims at disambiguating mentions to canonical entities from massive KBs, is essential for many tasks in natural language processing. Recent progress in entit…

cs.IR2021

DGEM: A New Dual-modal Graph Embedding Method in Recommendation System

Huimin Zhou, Qing Li, Yong Jiang +2

In the current deep learning based recommendation system, the embedding method is generally employed to complete the conversion from the high-dimensional sparse feature vector to t…

cs.CL20211 cited

Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings

Xinyu Wang, Zixia Jia, Yong Jiang +1

This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatena…

cs.CL20217 cited

Towards Emotional Support Dialog Systems

Siyang Liu, Chujie Zheng, Orianna Demasi +5

Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats. Following reasonable proce…