Recent advances in pre-trained language models (PLMs), such as GPT, BERT, and their larger variants, have significantly enhanced the ability to generate synthetic datasets for zero-shot learning, where models can generate data without the need for human annotations. However, existing methods often focus on generating domain-specific datasets, which restricts their generalization capabilities when applied to new, unseen domains. Furthermore, many of these methods rely on a single, static dataset that is either too broad or too narrowly tailored to a specific domain, making them less adaptable to diverse downstream domains. In this paper, we propose a dual prompt data generation framework, called Adaptive Domain-Specific Classification via Zero-shot Dataset Generation (AdaGen), which decouples universal representation learning from domain-specific adaptation. The framework consists of two components: domainagnostic training set generation via a universal prompt, followed by domain-specific validation set generation for weight learning. By combining these two components, our method achieves an optimal trade-off between cross-domain robustness and domainspecific accuracy, allowing a small task-aware model to effectively adapt across diverse domains while preserving generalization. This novel method overcomes the limitations of traditional methods by introducing dynamic feedback, enabling efficient zeroshot learning with improved adaptability. Extensive experiments in seven real-world datasets demonstrate the effectiveness of AdaGen, which outperforms the compared baseline models.

In the digital age, recommendation systems are crucial for enhancing user experiences, with bundle recommendations playing a key role by integrating complementary products. However, existing methods fail to accurately identify user preferences for specific items within bundles, making it difficult to design bundles containing more items of interest to users. Additionally, these methods do not leverage similar preferences among users of the same category, resulting in unstable and incomplete preference expressions. To address these issues, we propose Preference Identification by Interaction Overlap for Bundle Recommendation (PIIO). The data augmentation module analyzes the overlap between bundle-item inclusions and user-item interactions to calculate the interaction probability of non-interacted bundles, selecting the bundle with the highest probability as a positive sample to enrich user-bundle interactions and uncover user preferences for items within bundles. The preference aggregation module utilizes the overlap in user-item interactions to select similar users, aggregates preferences using an autoencoder, and constructs comprehensive preference profiles. The optimization module predicts user-bundle matching scores based on a user interest boundary loss function. The proposed PIIO model is applied to two bundle recommendation datasets, and experiments demonstrate the effectiveness of the PIIO model, surpassing state-of-the-art models.