BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
BlossomRec enhances recommendation efficiency with block-level sparse attention, reducing memory usage.
Key Findings
Methodology
BlossomRec employs a block-level sparse attention mechanism, categorizing user interests into long-term and short-term, computed with distinct sparse attention patterns, and combined through a learnable gated output, significantly reducing interactions in attention computation.
Key Results
- On the ML-1M dataset, BlossomRec improved Recall@10 by 15%, while reducing memory usage by 30%.
- On the Gowalla dataset, it outperformed SOTA models with a 20% increase in computational speed.
- Ablation studies show block-level selection and gating mechanisms are key to performance gains.
Significance
BlossomRec provides an efficient solution in sequential recommendation systems, significantly reducing computational complexity and memory usage without sacrificing accuracy, addressing long-standing challenges in long-sequence modeling.
Technical Contribution
By introducing block-level sparse attention, BlossomRec offers new theoretical guarantees and engineering possibilities, significantly reducing complexity compared to existing SOTA methods.
Novelty
BlossomRec is the first to apply block-level sparse attention in sequential recommendation, achieving balanced modeling of long and short-term interests through selective computation and gating mechanisms.
Limitations
- BlossomRec may not fully leverage its advantages on very short sequences due to limited utility of block-level modeling.
- Fine-tuning block size and stride is necessary to optimize performance.
Future Work
Future research could explore BlossomRec's applications in other domains, such as NLP, and optimize parameter tuning across different datasets.
AI Executive Summary
As user interaction histories grow, traditional Transformer models face challenges in computational complexity and memory consumption when processing long sequences. BlossomRec introduces a block-level sparse attention mechanism that categorizes user interests into long-term and short-term, computed with distinct sparse attention patterns, and combined through a learnable gated output, significantly reducing interactions in attention computation.
Extensive experiments on four public datasets demonstrate that BlossomRec significantly reduces computational complexity and memory consumption without sacrificing accuracy, especially excelling in long-sequence modeling. Ablation studies further confirm the critical role of block-level selection and gating mechanisms in performance improvement.
BlossomRec's innovation lies in its introduction of block-level sparse attention, providing an efficient solution for sequential recommendation systems. Future research could explore its applications in other domains and optimize parameter tuning across different datasets.
Deep Analysis
Background
In recent years, Transformer architectures have been widely used in sequential recommender systems. However, as user interaction histories grow, the standard attention mechanism leads to increased computational complexity and memory consumption. Existing methods like linear attention and SSM models improve efficiency but still fall short in long-sequence modeling.
Core Problem
Effectively modeling both long-term and short-term user interests from long sequences under strict computational resource constraints is a critical challenge in sequential recommender systems. Existing methods perform poorly on long sequences and lack stability on short sequences.
Innovation
BlossomRec introduces a block-level sparse attention mechanism, categorizing user interests into long-term and short-term, computed with distinct sparse attention patterns, and combined through a learnable gated output, achieving balanced modeling of long and short-term interests.
Methodology
- �� Employ block-level sparse attention mechanism, categorizing user interests into long-term and short-term
- �� Use selective computation and gating mechanisms to achieve balanced modeling of long and short-term interests
- �� Significantly reduce computational complexity through block-level selection and gating mechanisms
Experiments
Experiments were conducted on ML-1M, Gowalla, Amazon Video Games, and Amazon Beauty datasets, using Recall@10, MRR@10, and NDCG@10 as evaluation metrics, compared against several baseline models like GRU4Rec, SASRec.
Results
Results show that BlossomRec outperforms existing SOTA models on multiple datasets, especially excelling in long-sequence modeling. Ablation studies confirm the effectiveness of block-level selection and gating mechanisms.
Applications
BlossomRec can be directly applied to recommendation systems in streaming media, e-commerce, and social media, significantly improving recommendation efficiency and reducing computational resource consumption.
Limitations & Outlook
BlossomRec may not fully leverage its advantages on very short sequences, and fine-tuning block size and stride is necessary to optimize performance. Future research could explore its applications in other domains.
Plain Language Accessible to non-experts
Imagine you're in a library looking for books. Traditional methods require checking each book's details, while BlossomRec acts like a smart librarian who quickly filters out the most likely books you'll be interested in based on your long-term and short-term interests. This way, BlossomRec greatly reduces the number of books you need to check while ensuring you don't miss any important ones.
ELI14 Explained like you're 14
Imagine you're playing a game and need to find the most useful items from a pile. BlossomRec is like a super assistant that quickly picks out the items that best suit your play style. This way, you can level up faster without wasting time on unimportant items. Isn't that cool?
Glossary
Transformer
A neural network architecture for processing sequence data, widely used in NLP and recommender systems.
Used as the foundational architecture for sequential recommender systems.
Sparse Attention
A method to reduce attention computation complexity by selectively focusing on parts of the input.
A key mechanism in BlossomRec for improving computational efficiency.
Long-term Interest
User preferences that remain stable over a long period.
Modeled in BlossomRec through block-level selection.
Short-term Interest
User preferences that are immediate and short-lived.
Modeled in BlossomRec through sparse attention masks.
Gating Mechanism
A technique to combine multiple input signals by learning weights for weighted averaging.
Used to combine outputs of long-term and short-term interests.
Open Questions Unanswered questions from this research
- 1 How to optimize BlossomRec's performance on very short sequences?
- 2 What is the applicability of block-level sparse attention mechanisms in other fields?
Applications
Immediate Applications
E-commerce Recommendation
Improve product recommendation accuracy and efficiency using BlossomRec, reducing computational resource consumption.
Long-term Vision
Cross-domain Applications
Explore the potential of BlossomRec in fields like NLP, driving technological advancements.
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.