Feature decorrelation improves balance in sequential item recommendations
A Redundancy Reduction Approach for Controllable Sequential Recommendations
Information Retrieval
Summary
Items people see recommended often favor popular choices, making it hard for less-known items to appear. The authors studied a new way to reduce overlap in recommendation features, which helps spread attention more evenly across popular and less popular items. They created a training method that encourages this feature variety by comparing user histories with similar next-item interests. This method improved recommendation rankings and allows control over how much focus is given to popular versus less popular items.
What this means in practice
- •For e-commerce platform teams: Improve recommendation systems to better balance popular and niche product exposure using feature decorrelation techniques.
- •For streaming service engineers: Enhance next-item predictions in content recommendation systems to avoid popularity concentration and promote less-known media.
Authors
Veronika Ivanova, Marina Munkhoeva, Ivan Razvorotnev, Evgeny Frolov
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.