FUMO: Frequency Unified Embedding Optimization for Sequential Recommendation

Sequential recommendation systems suffer from highly imbalanced item frequency distribution in real-world datasets. This imbalance can contribute to popularity bias and significantly influences recommendation accuracy. Training trajectories show that frequent item accuracy plateaus early while rare items require continued training for accuracy improvement, revealing unequal learning progress under the shared training schedule. To solve this problem, we propose Frequency Unified eMbedding Optimization (FUMO), an optimizer that uses item frequency to adjust recommendation updates for item embeddings. FUMO applies larger updates for recommendation loss on rare items and combines frequency-dependent weight decay with adaptive shrinkage to control the overfitting, without modifying the model architecture or introducing additional loss functions. Across six sequential recommendation models and four datasets, FUMOshows compelling performance compared with other baseline optimizers and improves HR@20 up to 18.5% and NDCG@20 up to 25.0% over Adam. Training trajectories additionally show earlier improvements in rare item validation. These results support item frequency as a useful signal for adjusting embedding optimization across diverse sequential recommendation systems, and show that FUMO is a competitive optimizer for sequential recommendation.