BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations

TL;DR

BlossomRec introduces a block-level fused sparse attention mechanism for sequential recommendations, effectively modeling both long-term and short-term user interests while reducing computational costs and maintaining high performance.

Contribution

The paper proposes a novel sparse attention mechanism, BlossomRec, that improves efficiency and stability in modeling user interests across varying sequence lengths in recommender systems.

Findings

Achieves comparable or superior recommendation performance.

Reduces memory usage significantly.

Effective for both long and short user interaction sequences.

Abstract

Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements also grow. This is mainly caused by the standard attention mechanism. Although there exist many methods employing efficient attention and SSM-based models, these approaches struggle to effectively model long sequences and may exhibit unstable performance on short sequences. To address these challenges, we design a sparse attention mechanism, BlossomRec, which models both long-term and short-term user interests through attention computation to achieve stable performance across sequences of varying lengths. Specifically, we categorize user interests in recommendation systems into long-term and short-term interests, and compute them using two distinct sparse attention patterns, with the results combined through a learnable gated output. Theoretically, it significantly reduces the number of interactions participating in attention computation. Extensive experiments on four public datasets demonstrate that BlossomRec, when integrated with state-of-the-art Transformer-based models, achieves comparable or even superior performance while significantly reducing memory usage, providing strong evidence of BlossomRec's efficiency and effectiveness. The code is available at https://github.com/Applied-Machine-Learning-Lab/WWW2026_BlossomRec.