18 citations · 18 across the 2 of their papers we have counts for
2 papers
cs.IR2023
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
Juntao Tan, Shelby Heinecke, Zhiwei Liu +3
Sequential recommendation models, models that learn from chronological user-item interactions, outperform traditional recommendation models in many settings. Despite the success of…
cs.IR2022★ 18 cited
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li +3
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender s…