Unbiased Rectification for Sequential Recommender Systems Under Fake Orders
Summary: arXiv:2604.08550v1 Announce Type: cross
Abstract: Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactions, including click farming, context-irrelevant substitutions, and sequential perturbations.
Unlike injecting carefully designed fake users to influence recommendation performance, fake orders embedded within genuine user sequences aim to disrupt user preferences and mislead recommendation results, thereby manipulating exposure rates of specific items to gain competitive advantages. To protect users’ authentic interest preferences and eliminate misleading information, this paper aims to perform precise and efficient rectification on compromised sequential recommender systems while avoiding the enormous computational and time costs of retraining existing models.
Specifically, we identify that fake orders are not absolutely harmful; in certain cases, partial fake orders can even have a data augmentation effect. Based on this insight, we propose Dual-view Identification and Targeted Rectification (DITaR), which primarily identifies harmful samples to achieve unbiased rectification of the system.
Key Features of DITaR
- Differentiated Representations: The core idea of this method is to obtain differentiated representations from collaborative and semantic views for precise detection.
- Targeted Rectification: DITaR filters detected suspicious fake orders to select truly harmful ones for targeted rectification with gradient ascent.
- Preservation of Useful Information: This method ensures that useful information in fake orders is not removed while preventing bias residue.
- Maintaining System Performance: DITaR maintains the original data volume and sequence structure, thus protecting system performance and trustworthiness.
Experimental Results
Extensive experiments on three datasets demonstrate that DITaR achieves superior performance compared to state-of-the-art methods. The evaluation criteria focus on:
- Recommendation Quality: DITaR outperforms existing methods in delivering more accurate recommendations.
- Computational Efficiency: The method reduces the computational burden, making it faster and more efficient.
- System Robustness: DITaR enhances the system’s resilience against manipulation through fake orders.
Conclusion
As the threat of fake orders continues to evolve, maintaining the integrity of sequential recommender systems has never been more critical. The innovative approach of DITaR not only addresses the immediate challenges posed by fake orders but also sets a precedent for future research in the field of recommendation systems. By focusing on unbiased rectification, we can ensure a fairer and more reliable recommendation landscape for users, ultimately enhancing their experience and trust in these systems.