Sequential Data Augmentation for Generative Recommendation
GenPAS framework improves generative recommendation accuracy via three-step sampling, showing up to 783.7% performance boost.
Key Findings
Methodology
The GenPAS framework models data augmentation as a stochastic sampling process over input-target pairs, comprising three bias-controlled steps: sequence sampling, target sampling, and input sampling. By adjusting bias parameters, GenPAS flexibly shapes the training distribution, unifying existing strategies and enabling discovery of more effective configurations.
Key Results
- Experiments show that on the ML1M dataset, models using the Multi-Target strategy achieved a 783.7% performance improvement, while Slide-Window strategy generated the most input-target pairs but did not always perform best.
- On the Beauty dataset, GenPAS framework achieved the lowest KL divergence, indicating good alignment with test target distribution.
- By tuning parameters, GenPAS achieved significant accuracy and efficiency gains across multiple benchmark and industrial datasets.
Significance
This research provides a systematic framework for data augmentation in generative recommendation, addressing data sparsity issues and significantly enhancing model generalization and prediction accuracy. Its methodology offers practical guidance for efficient training data construction, with substantial academic and industrial impact.
Technical Contribution
GenPAS framework offers new theoretical guarantees in data augmentation, achieving precise shaping of training data distribution through flexible parameter control, surpassing existing state-of-the-art methods and opening new engineering possibilities.
Novelty
GenPAS is the first framework to systematically model data augmentation as a stochastic sampling process, providing a more flexible training data control mechanism compared to existing work, significantly boosting model performance.
Limitations
- On certain datasets, GenPAS requires complex parameter tuning, potentially demanding significant computational resources.
- The framework's performance in extreme data sparsity scenarios needs further validation.
- Long-term dependencies in user behavior sequences are not considered.
Future Work
Future research could explore automatic parameter tuning methods for GenPAS across different datasets and its application in real-time recommendation systems.
AI Executive Summary
Generative recommendation is crucial in personalized systems, yet existing methods lack systematic understanding in data augmentation. The GenPAS framework, with sequence, target, and input sampling steps, flexibly controls training data distribution, showing significant performance improvements across datasets.
The GenPAS framework unifies existing strategies and discovers more effective configurations by adjusting bias parameters. Experimental results indicate up to 783.7% performance improvement on the ML1M dataset and good alignment with test target distribution on the Beauty dataset.
This research provides a systematic framework for data augmentation in generative recommendation, addressing data sparsity issues and significantly enhancing model generalization and prediction accuracy. Its methodology offers practical guidance for efficient training data construction, with substantial academic and industrial impact.
Deep Analysis
Background
Generative recommendation systems predict users' future interactions to provide personalized services. However, data sparsity is a major challenge as users typically interact with only a small fraction of available items. Existing research has focused on model architecture or optimization techniques, lacking systematic understanding of data augmentation impacts.
Core Problem
Data augmentation in generative recommendation is often simplified or inconsistently applied, resulting in training data statistical properties that fail to effectively enhance model performance. Systematically constructing training data to improve model generalization is a key challenge.
Innovation
The GenPAS framework models data augmentation as a stochastic sampling process over input-target pairs, providing a flexible training data control mechanism. Its three-step sampling process precisely shapes the training distribution, unifying existing strategies and enabling discovery of more effective configurations.
Methodology
- �� Sequence Sampling: Select subsequences from user interaction sequences
- �� Target Sampling: Determine prediction targets for each subsequence
- �� Input Sampling: Select input data related to targets
Adjust bias parameters at each step to flexibly control training data distribution.
Experiments
Experiments utilize multiple benchmark and industrial datasets, including Beauty and ML1M, comparing model performance under different data augmentation strategies. Key metrics include accuracy, data efficiency, and parameter efficiency, with ablation studies analyzing strategy impacts.
Results
Results show GenPAS framework achieved up to 783.7% performance improvement on the ML1M dataset and good alignment with test target distribution on the Beauty dataset. Slide-Window strategy generated the most input-target pairs but did not always perform best.
Applications
The GenPAS framework can be applied in real-time recommendation systems, helping businesses improve recommendation accuracy and user satisfaction. Its flexible parameter control mechanism suits various datasets and application scenarios.
Limitations & Outlook
GenPAS requires complex parameter tuning, potentially demanding significant computational resources. Long-term dependencies in user behavior sequences are not considered, and performance in extreme data sparsity scenarios needs further validation.
Plain Language Accessible to non-experts
Imagine you're shopping in a large supermarket, and the recommendation system predicts products you might be interested in based on your shopping history. GenPAS acts like a smart store assistant, flexibly adjusting recommendation strategies based on your shopping habits, ensuring you always discover new and exciting products. This system analyzes your shopping history, selects suitable products as recommendation targets, and adjusts strategies accordingly.
ELI14 Explained like you're 14
Imagine you're playing a game that recommends the next level based on your history. GenPAS is like a smart game assistant, adjusting recommendation strategies based on your gaming habits, ensuring you always discover new levels. It analyzes your game history, selects suitable levels as targets, and adjusts strategies accordingly.
Glossary
Generative Recommendation
A system that predicts users' future interactions to provide personalized services.
Used to predict future interactions in personalized systems.
Data Augmentation
The process of constructing training data to enhance model performance.
Used in training data construction for generative recommendation.
Sequence Sampling
The process of selecting subsequences from user interaction sequences.
First step in GenPAS framework's sampling process.
Target Sampling
The process of determining prediction targets for each subsequence.
Second step in GenPAS framework's sampling process.
Input Sampling
The process of selecting input data related to targets.
Third step in GenPAS framework's sampling process.
Open Questions Unanswered questions from this research
- 1 How to optimize GenPAS framework performance in extreme data sparsity scenarios requires further research.
- 2 GenPAS's performance in long-term dependencies needs validation; future research could explore its potential.
Applications
Immediate Applications
Real-time Recommendation Systems
Businesses can use GenPAS framework to enhance recommendation accuracy and user satisfaction, suitable for e-commerce platforms.
Personalized Advertising
Advertisers can flexibly adjust ad strategies based on user history, improving ad effectiveness.
Long-term Vision
Smart Shopping Assistant
In the future, GenPAS could become a smart shopping assistant, helping users discover new products while shopping.
Abstract
Generative recommendation plays a crucial role in personalized systems, predicting users' future interactions from their historical behavior sequences. A critical yet underexplored factor in training these models is data augmentation, the process of constructing training data from user interaction histories. By shaping the training distribution, data augmentation directly and often substantially affects model generalization and performance. Nevertheless, in much of the existing work, this process is simplified, applied inconsistently, or treated as a minor design choice, without a systematic and principled understanding of its effects. Motivated by our empirical finding that different augmentation strategies can yield large performance disparities, we conduct an in-depth analysis of how they reshape training distributions and influence alignment with future targets and generalization to unseen inputs. To systematize this design space, we propose GenPAS, a generalized and principled framework that models augmentation as a stochastic sampling process over input-target pairs with three bias-controlled steps: sequence sampling, target sampling, and input sampling. This formulation unifies widely used strategies as special cases and enables flexible control of the resulting training distribution. Our extensive experiments on benchmark and industrial datasets demonstrate that GenPAS yields superior accuracy, data efficiency, and parameter efficiency compared to existing strategies, providing practical guidance for principled training data construction in generative recommendation. Our code is available at https://github.com/snap-research/GenPAS.