TRUST: Item-Calibrated Interval Evidence for Temporal Session-Based Recommendation

• URL: http://arxiv.org/abs/2606.27214v1
• Date: Thu, 25 Jun 2026 16:05:36 GMT
• Title: TRUST: Item-Calibrated Interval Evidence for Temporal Session-Based Recommendation
• Abstract summary: We propose a framework that evaluates each observed interval relative to the empirical interval distribution of the corresponding item.<n>Experiments show that the proposed scoring function can improve existing temporal session recommenders as a model-agnostic method.
• Score: 9.041970067606773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal signals have been widely used in session-based recommendation to infer user interest. Existing temporal session-based recommenders primarily rely on absolute interval values, implicitly assuming that the same interval carries similar interest signals across items. However, we empirically find that this assumption does not hold: each item has its own interval distribution, so an interval should be interpreted relative to the item it belongs to. Based on this observation, we propose TRUST, a framework that evaluates each observed interval relative to the empirical interval distribution of the corresponding item. Specifically, we propose a score function to guide global neighbor sampling, session graph encoding, and final interest aggregation. Experiments on public datasets show that TRUST consistently improves over representative temporal and non-temporal baselines, and plug-in experiments further show that the proposed scoring function can improve existing temporal session recommenders as a model-agnostic method. Component-wise ablations further show that calibrating the temporal signals within each module, rather than removing the module itself, consistently improves neighbor sampling, session graph encoding, and interest aggregation.

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