Generative Adversarial Framework for Cold-Start Item Recommendation

1 code implementation • SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2022 Pages 2565–2571 2022 • Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li Authors Info & Claims

Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.

Stable Adversarial Learning under Distributional Shifts

no code implementations • 8 Jun 2020 • Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin

Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data.

SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems

no code implementations • 9 May 2020 • Qiaoan Chen, Hao Gu, Lingling Yi, Yishi Lin, Peng He, Chuan Chen, Yangqiu Song

Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.

Graph Attention Recommendation Systems

Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision

no code implementations • 16 Jan 2020 • Li Ye, Yishi Lin, Hong Xie, John C. S. Lui

A typical alternative is offline causal inference, which analyzes logged data alone to make decisions.

Causal Inference