NewEvery arXiv paper, its researchers & institutions — mapped.
papers

Publications (16)

cs.IR2019

Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

Lixin Zou, Long Xia, Zhuoye Ding +3

cs.IR2019

Deep Social Collaborative Filtering

Wenqi Fan, Yao Ma, Dawei Yin +3

cs.CL2018

Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation

Xisen Jin, Wenqiang Lei, Zhaochun Ren +4

cs.SI2017

Preserving Local and Global Information for Network Embedding

Yao Ma, Suhang Wang, ZhaoChun Ren +2

stat.ML2019

Off-policy Learning for Multiple Loggers

Li He, Long Xia, Wei Zeng +3

The paper develops methods for off‑policy learning using data collected from multiple historical policies (multiple loggers), introducing a generalization‑error‑based risk function…

#off-policy learning#multiple loggers#counterfactual estimation#constrained optimization
cs.CL2019

Product-Aware Answer Generation in E-Commerce Question-Answering

Shen Gao, Zhaochun Ren, Yihong Eric Zhao +3

stat.ML2015

Consistent Collective Matrix Completion under Joint Low Rank Structure

Suriya Gunasekar, Makoto Yamada, Dawei Yin +1

cs.LG2018

Streaming Graph Neural Networks

Yao Ma, Ziyi Guo, Zhaochun Ren +3

cs.SI2018

Multi-dimensional Graph Convolutional Networks

Yao Ma, Suhang Wang, Charu C. Aggarwal +2

stat.ML2016

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10

cs.LG2019

Deep Reinforcement Learning for List-wise Recommendations

Xiangyu Zhao, Liang Zhang, Long Xia +3

cs.IR2018

Deep Reinforcement Learning for Page-wise Recommendations

Xiangyu Zhao, Long Xia, Liang Zhang +3

cs.CL2018

Linked Recurrent Neural Networks

Zhiwei Wang, Yao Ma, Dawei Yin +1

stat.ML2014

N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

Makoto Yamada, Avishek Saha, Hua Ouyang +2

cs.SI2016

Streaming Recommender Systems

Shiyu Chang, Yang Zhang, Jiliang Tang +4

cs.IR2018

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

Xiangyu Zhao, Liang Zhang, Zhuoye Ding +3