MLSA4Rec: Mamba Combined with Low-Rank Decomposed Self-Attention for Sequential Recommendation
MLSA4Rec combines Mamba with low-rank self-attention to enhance recommendation accuracy.
Key Findings
Methodology
MLSA4Rec integrates Mamba with low-rank self-attention modules, creating a linear complexity framework. Mamba injects structural bias, while the low-rank self-attention module analyzes user preferences and dynamically guides Mamba to focus on critical information.
Key Results
- On MovieLens-1M, MLSA4Rec achieved HR@10 of 0.3263, significantly outperforming other methods.
- On Amazon-Beauty, MLSA4Rec's NDCG@10 was 0.0465, better than SASRec and BERT4Rec.
- Ablation studies show that the combination of Mamba and self-attention mechanisms is most effective.
Significance
This research addresses complexity issues in sequential recommendation by combining Mamba and self-attention mechanisms, enhancing recommendation accuracy with broad application potential.
Technical Contribution
MLSA4Rec reduces computational complexity through low-rank decomposition self-attention and injects structural bias via Mamba, offering new theoretical guarantees and engineering possibilities.
Novelty
This is the first study to combine Mamba with self-attention for sequential recommendation, providing an innovative solution that breaks the complexity bottleneck of existing methods.
Limitations
- MLSA4Rec may still face efficiency issues when handling extremely long sequences.
- The model's robustness to noisy data needs further validation.
- Application effectiveness in certain domains remains to be evaluated.
Future Work
Future research can optimize the Mamba-LSA interaction module and explore its applications in other recommendation tasks.
AI Executive Summary
Sequential recommendation systems are crucial in e-commerce, online education, and streaming services. However, self-attention-based models, despite their excellent performance, suffer from high complexity and lack structural bias. MLSA4Rec combines Mamba with low-rank self-attention modules, creating a linear complexity framework. Experimental results show that MLSA4Rec outperforms existing methods across multiple real-world datasets, demonstrating the great potential of combining Mamba with self-attention. Future research can further optimize this framework and explore its applications in other recommendation tasks.
Deep Analysis
Background
Sequential recommendation systems play a vital role in predicting the next possible user interaction by analyzing historical data. Traditional methods like collaborative filtering and matrix factorization perform poorly with long sequences and sparse data. Deep learning methods, such as CNNs and RNNs, have improved performance but still face limitations in capturing sequential order information.
Core Problem
Self-attention mechanisms, while effective at capturing dependencies between user interaction items, suffer from quadratic complexity, limiting their application to long sequences. Additionally, the lack of structural bias makes models prone to overfitting noise.
Innovation
MLSA4Rec combines Mamba with low-rank self-attention, creating a linear complexity framework. Mamba injects structural bias, while low-rank self-attention analyzes user preferences and dynamically guides Mamba to focus on critical information.
Methodology
- �� Use Mamba module to inject structural bias
- �� Analyze user preferences with low-rank self-attention
- �� Guide Mamba through gated information transmission
- �� Combine refined user preference information for prediction
Experiments
Experiments were conducted on MovieLens-1M, Amazon-Beauty, and Amazon-Video-Games datasets, using HR@10, NDCG@10, and MRR@10 as evaluation metrics. Comparison methods included GRU4Rec, NARM, SASRec, BERT4Rec, and Mamba4Rec.
Results
MLSA4Rec achieved HR@10 of 0.3263 on MovieLens-1M, significantly outperforming other methods. On Amazon-Beauty, MLSA4Rec's NDCG@10 was 0.0465, better than SASRec and BERT4Rec. Ablation studies show that the combination of Mamba and self-attention mechanisms is most effective.
Applications
MLSA4Rec can be used for personalized recommendations in e-commerce, course recommendations in online education, and content recommendations in streaming services, enhancing user satisfaction and platform revenue.
Limitations & Outlook
MLSA4Rec may still face efficiency issues when handling extremely long sequences. The model's robustness to noisy data needs further validation. Application effectiveness in certain domains remains to be evaluated.
Plain Language Accessible to non-experts
Imagine you're in a library, looking for the next book you might enjoy. MLSA4Rec acts like a smart assistant that not only remembers the books you've borrowed before but also predicts what you might like next based on that information. It uses a technique called Mamba to filter out irrelevant information and employs low-rank self-attention to analyze your reading preferences.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game and the system recommends the next level. MLSA4Rec is like a super-smart game assistant that predicts what level you might like next based on your past gaming history. It uses a technique called Mamba to filter out unimportant stuff and low-rank self-attention to analyze your gaming style. Isn't that cool?
Glossary
Mamba
A linear complexity state space model that filters noise and retains relevant information.
Used to inject structural bias and improve recommendation accuracy.
Self-Attention
A mechanism for calculating dependencies between items in a sequence.
Used to capture dependencies in user historical interactions.
Low-Rank Decomposition
A technique that reduces matrix dimensions to lower computational complexity.
Used to reduce the complexity of self-attention mechanisms.
HR@10
Hit rate metric indicating the proportion of target items hit in the top 10 recommendations.
Used to evaluate recommendation system accuracy.
NDCG@10
Normalized Discounted Cumulative Gain, measuring the quality of recommendation rankings.
Used to evaluate recommendation system ranking effectiveness.
Open Questions Unanswered questions from this research
- 1 How to further optimize the Mamba-LSA interaction module for efficiency?
- 2 How to ensure model robustness when handling extremely long sequences?
Applications
Immediate Applications
E-commerce Recommendation
MLSA4Rec can be used for personalized recommendations on e-commerce platforms, enhancing user satisfaction.
Long-term Vision
Intelligent Education Systems
Develop intelligent education systems using MLSA4Rec technology for personalized learning path recommendations.
Abstract
In applications such as e-commerce, online education, and streaming services, sequential recommendation systems play a critical role. Despite the excellent performance of self-attention-based sequential recommendation models in capturing dependencies between items in user interaction history, their quadratic complexity and lack of structural bias limit their applicability. Recently, some works have replaced the self-attention module in sequential recommenders with Mamba, which has linear complexity and structural bias. However, these works have not noted the complementarity between the two approaches. To address this issue, this paper proposes a new hybrid recommendation framework, Mamba combined with Low-Rank decomposed Self-Attention for Sequential Recommendation (MLSA4Rec), whose complexity is linear with respect to the length of the user's historical interaction sequence. Specifically, MLSA4Rec designs an efficient Mamba-LSA interaction module. This module introduces a low-rank decomposed self-attention (LSA) module with linear complexity and injects structural bias into it through Mamba. The LSA module analyzes user preferences from a different perspective and dynamically guides Mamba to focus on important information in user historical interactions through a gated information transmission mechanism. Finally, MLSA4Rec combines user preference information refined by the Mamba and LSA modules to accurately predict the user's next possible interaction. To our knowledge, this is the first study to combine Mamba and self-attention in sequential recommendation systems. Experimental results show that MLSA4Rec outperforms existing self-attention and Mamba-based sequential recommendation models in recommendation accuracy on three real-world datasets, demonstrating the great potential of Mamba and self-attention working together.