OpenNovelty.org

Dataset Regeneration for Cross Domain Recommendation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Recommender SystemCross-domain recommendationDataset Regeneration
Abstract:

Cross-domain recommendation (CDR) has emerged as an effective strategy to mitigate data sparsity and cold-start challenges by transferring knowledge from a source domain to a target domain. Despite recent progress, two key issues remain: (i) Sparse overlap. In real-world datasets such as Amazon, the proportion of users active in both domains is extremely low, significantly limiting the effectiveness of many state-of-the-art CDR approaches. (ii) Negative transfer. Existing methods primarily address this problem at the model level, often assuming that logged interactions are unbiased and noise-free. In practice, however, recommender data contain numerous spurious correlations, and this issue is exacerbated in CDR due to domain heterogeneity. To address these challenges, we propose a dataset regeneration framework. First, we leverage a prediction model to generate a pool of high-confidence candidate interactions to link non-overlapping target-domain users and source-domain items. Second, inspired by causal inference, we introduce a filtering process designed to prune spurious interactions. This process identifies and removes not only noisy edges created during generation but also those from the original dataset, retaining only the interactions that have a positive causal effect on the target-domain performance. Through these two processes, we can regenerate a source-domain dataset that exhibits a tighter coupling and a more explicit causal connection with the target domain. By integrating our method with three representative recommendation backbones—LightGCN, BiTGCF, and CUT—we show that it significantly boosts their predictive accuracy on the target domain, achieving substantial gains of up to 23.81% in Recall@10 and 22.22% in NDCG@10.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a dataset regeneration framework for cross-domain recommendation that addresses sparse overlap and negative transfer through a generate-and-filter approach. It sits in the 'Dataset Regeneration and Filtering' leaf under 'Data-Level Interventions', where it is currently the sole paper. This positioning reflects a relatively sparse research direction within the broader taxonomy, which contains 26 papers across multiple branches. The work's focus on data-level manipulation distinguishes it from the more populated 'Knowledge Transfer Mechanisms' branch, which emphasizes architectural designs for embedding alignment and latent space sharing.

The taxonomy reveals neighboring directions that contextualize this work. 'Contrastive Data Augmentation' (one paper) explores self-supervised augmentation methods, while 'Causal Inference and Debiasing' (two papers) addresses bias through causal modeling. The 'Knowledge Transfer Mechanisms' branch is more densely populated with bidirectional and unidirectional architectures (seven papers total), suggesting that model-level transfer has received more attention than data-level interventions. The paper's dual emphasis on generation and causal filtering bridges these areas, connecting data augmentation with causal reasoning in a way that appears less explored in the current taxonomy structure.

Among 22 candidates examined, the self-supervised generation module (Contribution 2) shows potential overlap with one prior work among four candidates reviewed. The generate-and-filter framework (Contribution 1) and counterfactual filtering process (Contribution 3) examined eight and ten candidates respectively, with no clear refutations found. These statistics suggest that while the generation component may have precedent in limited prior work, the overall framework combining generation with causal filtering appears less directly addressed in the examined literature. The modest search scope (22 papers) means these findings reflect top-K semantic matches rather than exhaustive coverage.

Based on the limited search scope, the work appears to occupy a relatively underexplored intersection of data augmentation and causal filtering for cross-domain recommendation. The taxonomy structure confirms that data-level interventions receive less attention than architectural approaches, and the paper's position as the sole occupant of its leaf suggests a distinct methodological angle. However, the single refutable candidate for the generation module indicates that components of the approach may connect to existing augmentation techniques, warranting careful positioning relative to prior data synthesis methods.

Taxonomy

Core-task Taxonomy Papers
26
3
Claimed Contributions
22
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: dataset regeneration for cross-domain recommendation. Cross-domain recommendation addresses the challenge of leveraging knowledge from multiple domains to improve recommendation quality, particularly when data in a target domain is sparse. The field's structure, as reflected in the taxonomy, spans several complementary directions. Knowledge Transfer Mechanisms and User Representation Learning focus on how to share and encode user preferences across domains, often through embedding alignment or shared latent spaces. Data-Level Interventions and Dataset Regeneration and Filtering emphasize direct manipulation of training data—augmenting, filtering, or synthesizing samples to bridge domain gaps. Causal Inference and Debiasing tackle selection bias and confounding, while Domain Bridging Strategies and Privacy-Preserving Cross-Domain Methods address the practical challenges of aligning heterogeneous data sources and protecting user information. Fairness and Non-Overlapping User Handling, along with Cross-Domain Architectures and Benchmarking, round out the landscape by ensuring equitable treatment of diverse user groups and providing standardized evaluation frameworks. Within Data-Level Interventions, a handful of works explore how to regenerate or augment datasets to improve cross-domain transfer. Cross-reconstructed Augmentation[3] and Automated Self-Supervised[4] methods generate synthetic samples or augment existing data to enrich sparse domains, while Equivalent Transformation[5] reframes data to facilitate transfer. Dataset Regeneration[0] sits squarely in this cluster, emphasizing the creation of new training instances tailored to cross-domain scenarios. Compared to Cross-reconstructed Augmentation[3], which focuses on reconstruction-based augmentation, Dataset Regeneration[0] may adopt a more direct synthesis or filtering strategy. Meanwhile, Causality Enhancement[2] and Deep Dual Transfer[1] illustrate adjacent themes—causal reasoning and dual-network architectures—that complement data regeneration by addressing why and how to transfer knowledge. The interplay between data augmentation, causal modeling, and architectural design remains an active area, with open questions about the optimal balance between synthetic data quality and transfer effectiveness.

Claimed Contributions

Generate-and-filter dataset regeneration framework for CDR

The authors introduce a data-level framework that addresses sparse overlap and negative transfer in cross-domain recommendation by regenerating the source dataset. This framework operates through two processes: generating high-confidence candidate interactions and filtering spurious interactions using causal inference principles.

8 retrieved papers
Self-supervised generation module for synthetic interactions

A self-supervised prediction model is pretrained to generate synthetic interactions in the source domain for users who only appear in the target domain. This augments cross-domain connections by creating a pool of high-confidence candidate interactions that bridge the domain gap.

4 retrieved papers
Can Refute
Counterfactual filtering process for causal interaction identification

The authors develop a filtering mechanism inspired by causal inference that uses counterfactual evaluation to identify which source-domain interactions have genuine causal effects on target-domain performance. This process removes both noisy generated edges and spurious correlations from the original dataset.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Generate-and-filter dataset regeneration framework for CDR

The authors introduce a data-level framework that addresses sparse overlap and negative transfer in cross-domain recommendation by regenerating the source dataset. This framework operates through two processes: generating high-confidence candidate interactions and filtering spurious interactions using causal inference principles.

Contribution

Self-supervised generation module for synthetic interactions

A self-supervised prediction model is pretrained to generate synthetic interactions in the source domain for users who only appear in the target domain. This augments cross-domain connections by creating a pool of high-confidence candidate interactions that bridge the domain gap.

Contribution

Counterfactual filtering process for causal interaction identification

The authors develop a filtering mechanism inspired by causal inference that uses counterfactual evaluation to identify which source-domain interactions have genuine causal effects on target-domain performance. This process removes both noisy generated edges and spurious correlations from the original dataset.

Table of Contents