# Generative Data Transformation: From Mixed to Unified Data

arXiv: https://arxiv.org/abs/2602.22743 · HTML: https://mlsanity.com/paper/2602.22743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 52 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 30 of 34)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Contrastive Decoding: Open-ended Text Generation as Optimization](https://mlsanity.com/paper/2210.15097/) (2022)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [Global Context Enhanced Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2106.05081/) (2021)
- [A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems](https://mlsanity.com/paper/2308.08434/) (2023)
- [Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer](https://mlsanity.com/paper/2105.00522/) (2021)
- [Diffusion Augmentation for Sequential Recommendation](https://mlsanity.com/paper/2309.12858/) (2023)
- [DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2105.03300/) (2021)
- [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023)
- [A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions](https://mlsanity.com/paper/2108.03357/) (2021)
- [Mixed Information Flow for Cross-domain Sequential Recommendations](https://mlsanity.com/paper/2012.00485/) (2020)
- [Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations](https://mlsanity.com/paper/1910.02448/) (2019)
- [Infinite Recommendation Networks: A Data-Centric Approach](https://mlsanity.com/paper/2206.02626/) (2022)
- [Triple Sequence Learning for Cross-domain Recommendation](https://mlsanity.com/paper/2304.05027/) (2023)
- [Dataset Regeneration for Sequential Recommendation](https://mlsanity.com/paper/2405.17795/) (2024)
- [MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation](https://mlsanity.com/paper/2304.08382/) (2023)
- [Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent](https://mlsanity.com/paper/2506.23485/) (2025)
- [RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation](https://mlsanity.com/paper/2111.10093/) (2021)
- [A Survey on Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2401.04971/) (2024)
- [Mixed Attention Network for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2311.08272/) (2023)
- [Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2407.11245/) (2024)
- [TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation](https://mlsanity.com/paper/2502.02854/) (2025)
- [Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM](https://mlsanity.com/paper/2411.01610/) (2024)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities](https://mlsanity.com/paper/2205.10759/) (2022)
- [TF-DCon: Leveraging Large Language Models (LLMs) to Empower Training-Free Dataset Condensation for Content-Based Recommendation](https://mlsanity.com/paper/2310.09874/) (2023)
- [Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction](https://mlsanity.com/paper/2411.15005/) (2024)
- [UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation](https://mlsanity.com/paper/2504.20500/) (2025)

## Abstract (arXiv)

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 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 textscTaesar, a data-centric framework for target-aligned sequential regeneration, 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 textscTaesar outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, textscTaesar effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ textcolorbluehttps://github.com/USTC-StarTeam/Taesar.