On the Accuracy Limits of Sequential Recommender Systems: An Entropy-Based Approach

• URL: http://arxiv.org/abs/2603.27952v1
• Date: Mon, 30 Mar 2026 01:57:30 GMT
• Title: On the Accuracy Limits of Sequential Recommender Systems: An Entropy-Based Approach
• Abstract summary: We develop an entropy-induced, training-free approach for quantifying accuracy limits in sequential recommendation.<n> Experiments on controlled synthetic generators and diverse real-world benchmarks show that the estimator tracks oracle-controlled difficulty more faithfully than baselines.<n>It also supports user-group diagnostics by stratifying users by novelty preference, long-tail exposure, and activity.
• Score: 14.127575297140622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommender systems have achieved steady gains in offline accuracy, yet it remains unclear how close current models are to the intrinsic accuracy limit imposed by the data. A reliable, model-agnostic estimate of this ceiling would enable principled difficulty assessment and headroom estimation before costly model development. Existing predictability analyses typically combine entropy estimation with Fano's inequality inversion; however, in recommendation they are hindered by sensitivity to candidate-space specification and distortion from Fano-based scaling in low-predictability regimes. We develop an entropy-induced, training-free approach for quantifying accuracy limits in sequential recommendation, yielding a candidate-size-agnostic estimate. Experiments on controlled synthetic generators and diverse real-world benchmarks show that the estimator tracks oracle-controlled difficulty more faithfully than baselines, remains insensitive to candidate-set size, and achieves high rank consistency with best-achieved offline accuracy across state-of-the-art sequential recommenders (Spearman rho up to 0.914). It also supports user-group diagnostics by stratifying users by novelty preference, long-tail exposure, and activity, revealing systematic predictability differences. Furthermore, predictability can guide training data selection: training sets constructed from high-predictability users yield strong downstream performance under reduced data budgets. Overall, the proposed estimator provides a practical reference for assessing attainable accuracy limits, supporting user-group diagnostics, and informing data-centric decisions in sequential recommendation.

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