Deleting each user or item from training data and retraining the model shows which observations help or hurt a recommender's overall performance, a straightforward application of leave-one-out influence analysis.
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Model-agnostic post-hoc explainability for recommender systems
Deleting each user or item from training data and retraining the model shows which observations help or hurt a recommender's overall performance, a straightforward application of leave-one-out influence analysis.