A Redundancy Reduction Approach for Controllable Sequential Recommendations
TL;DR
This work proposes a decorrelation-regularized training framework that augments next-item prediction with an auxiliary redundancy-reduction term, and instantiate it with BT-SR, which uses the Barlow Twins objective to form label-consistent positive pairs without synthetic corruptions.
Abstract
Sequential recommendation must operate under long-tailed item distributions and popularity-driven concentration, often forcing practitioners to trade short-list accuracy against long-tail exposure. In this work, we study feature decorrelation as a mechanism for shaping representation geometry in dot‑product sequential recommenders, and analyze how this, in turn, affects popularity‑driven concentration. We propose a decorrelation-regularized training framework that augments next-item prediction with an auxiliary redundancy-reduction term, and instantiate it with BT-SR, which uses the Barlow Twins objective. To form label-consistent positive pairs without synthetic corruptions, we pair user histories that share the same next-item target. Beyond accuracy, we provide a geometric analysis suggesting how decorrelation suppresses shared low-rank directions in the user representation space that may give popular items a global scoring advantage, and we introduce a bucket-based alignment concentration metric to quantify this effect. Experiments on five public benchmarks show that BT-SR consistently improves next-item ranking quality, while the decorrelation strength acts as a simple control knob that reallocates accuracy across head and tail items, enabling accuracy–exposure trade‑offs. Our analysis also reveals that the impact on head‑vs‑tail exposure differs across datasets, reflecting interactions between decorrelation and data temporal structure.