21 citations · 29 across the 4 of their papers we have counts for
4 papers
Learning to Counterfactually Explain Recommendations
Yuanshun Yao, Chong Wang, Hang Li
Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not…
Counterfactually Evaluating Explanations in Recommender Systems
Yuanshun Yao, Chong Wang, Hang Li
Modern recommender systems face an increasing need to explain their recommendations. Despite considerable progress in this area, evaluating the quality of explanations remains a si…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen +3
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) a…