Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders

Modern sequential recommender systems, ranging from lightweight transformer-based variants to large language models, have become increasingly prominent in academia and industry due to their strong performance in the next-item prediction task.Yet common evaluation protocols for sequential recommendations remain insufficiently developed: they often fail to reflect the corresponding recommendation task accurately, or are not aligned with real-world scenarios.Although the widely used leave-one-out split matches next-item prediction, it permits the overlap between training and test periods, which leads to temporal leakage and unrealistically long test horizon, limiting real-world relevance.Global temporal splitting addresses these issues by evaluating on distinct future periods.However, its applications to sequential recommendations remain