Global temporal splits with Last or Random target selection correlate strongly with realistic successive evaluation, while leave-one-out splits produce inconsistent model rankings across datasets.
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Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
Global temporal splits with Last or Random target selection correlate strongly with realistic successive evaluation, while leave-one-out splits produce inconsistent model rankings across datasets.