A Redundancy Reduction Approach for Controllable Sequential Recommendations

2026-09-20

AI Summary

本文提出一种通过特征解相关减少冗余的方法，以解决序列推荐中长尾项目曝光不足的问题，并通过Barlow Twins目标实现，改善了推荐的准确性和长尾项目的曝光度。

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 showing how decorrelation suppresses shared low-rank directions in the user representation space that can 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.

Problem

• Sequential Recommendation
• Long-tailed Distribution
• Popularity-driven Concentration
• Redundancy Reduction
• Feature Decorrelation

Innovation

• Feature Decorrelation
• Redundancy Reduction
• Barlow Twins
• Sequential Recommendations
• Popularity-Driven Concentration