Generative Data Transformation: From Mixed to Unified Data
arXiv:2602.22743v1 Announce Type: new Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing \emph{model-centric} paradigm -- which relies on complex, customized architectures -- struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textsc{Taesar}, a \emph{data-centric} framework for \textbf{t}arget-\textbf{a}lign\textbf{e}d \textbf{s}equenti\textbf{a}l \textbf{r}egeneration, which employs a contrastive decoding mechanism to adaptiv
arXiv:2602.22743v1 Announce Type: new Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing \emph{model-centric} paradigm -- which relies on complex, customized architectures -- struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textsc{Taesar}, a \emph{data-centric} framework for \textbf{t}arget-\textbf{a}lign\textbf{e}d \textbf{s}equenti\textbf{a}l \textbf{r}egeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show \textsc{Taesar} outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, \textsc{Taesar} effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ \textcolor{blue}{https://github.com/USTC-StarTeam/Taesar}.
Executive Summary
The article proposes a novel data-centric framework, Taesar, to address challenges in recommendation model training data. By employing contrastive decoding, Taesar leverages cross-domain context to adaptively encode target-domain sequences. Experimental results demonstrate Taesar's superiority over model-centric solutions, showcasing its ability to learn intricate dependencies without complex fusion architectures. This framework effectively combines the strengths of data- and model-centric paradigms by generating enriched datasets.
Key Points
- ▸ Taesar is a data-centric framework for target-aligned sequential regeneration
- ▸ Contrastive decoding mechanism adapts cross-domain context into target-domain sequences
- ▸ Experiments show Taesar outperforms model-centric solutions and generalizes to various sequential models
Merits
Strength in addressing domain gaps
Taesar's data-centric approach effectively mitigates the degradative effects of domain gaps, leading to improved model performance.
Efficient computational requirements
Taesar's standard model architecture eliminates the need for complex fusion architectures, reducing computational demands.
Flexibility in model generalization
Taesar's framework generalizes to various sequential models, making it a versatile solution for recommendation model training data.
Demerits
Potential overfitting to specific datasets
Taesar's reliance on contrastive decoding may lead to overfitting to specific datasets, compromising its ability to generalize to new scenarios.
Training complexity and computational requirements
While Taesar reduces computational demands, its training process may still be complex and require substantial computational resources.
Limited exploration of data augmentation techniques
The article primarily focuses on Taesar's data-centric approach, leaving room for further exploration of data augmentation techniques to enhance its performance.
Expert Commentary
Taesar represents a significant advancement in the field of recommendation systems and sequential data processing. By leveraging contrastive decoding to adapt cross-domain context, Taesar effectively addresses the challenges of domain gaps and improves model performance. However, further research is needed to explore its potential limitations, particularly in regards to overfitting and training complexity. Additionally, the incorporation of data augmentation techniques could enhance Taesar's performance and adaptability. As a data-centric framework, Taesar offers a promising solution for recommendation model training data, and its implications for policy and practice are substantial.
Recommendations
- ✓ Further research should focus on exploring data augmentation techniques to enhance Taesar's performance and adaptability.
- ✓ The use of Taesar in real-world applications should be closely monitored to assess its practical implications and potential limitations.
