TRUST: Item-Calibrated Interval Evidence for Temporal Session-Based Recommendation
For session-based recommendation, this work addresses the overlooked item-specific temporal dynamics, offering a model-agnostic improvement that can be integrated into existing systems.
TRUST proposes a framework that calibrates temporal intervals relative to each item's own interval distribution, improving session-based recommendation. Experiments show consistent gains over baselines, with up to 10% improvement in recall and MRR on public datasets.
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.