Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation

• URL: http://arxiv.org/abs/2605.28493v1
• Date: Wed, 27 May 2026 13:51:23 GMT
• Title: Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation
• Abstract summary: Sequential recommendation effectively models dynamic user interests but continues to face challenges related to data sparsity.<n>We propose an adaptive future learning framework, UFRec, which encourages the model to look further ahead when it is confident in the current state.<n>Our method significantly outperforms state-of-the-art approaches by effectively leveraging future data.
• Score: 15.885719428802382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential recommendation effectively models dynamic user interests but continues to face challenges related to data sparsity. While self-supervised learning has alleviated this issue to some extent, most existing methods focus exclusively on immediate next-item prediction during training, thereby neglecting the rich information embedded in longer-term future interactions. Although a few studies have explored the utilization of future data, existing attempts typically apply future supervision signals with uniform intensity across all samples, which may lead to suboptimal solutions. In this paper, we propose an adaptive future learning framework, UFRec, which encourages the model to look further ahead when it is confident in the current state, while focusing on the immediate task when it is uncertain. Specifically, UFRec incorporates an Uncertainty-Guided Future Supervision module that dynamically modulates the weight of multi-step future supervision based on the model's confidence in the primary next-item prediction task. Furthermore, we complement step-wise future supervision with a Future-Aware Contrastive Learning module that treats the future trajectory as a holistic entity. Notably, both auxiliary modules are utilized exclusively during training and incur no inference overhead. Extensive experiments on four benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches by effectively leveraging future data.

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