Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the critical aspect of system robustness. Our experiments reveal that while LLM-driven methods excel on standard datasets, their accuracy is notably compromised when faced with adversarial perturbations. To address this challenge, we propose a robust text-to-SQL solution, called Solid-SQL, designed to integrate with various LLMs. We focus on the pre-processing stage, training a robust schema-linking model enhanced by LLM-based data augmentation. Additionally, we design a two-round, structural similarity-based example retrieval strategy for in-context learning. Our method achieves SOTA SQL execution accuracy levels of 82.1% and 58.9% on the general Spider and Bird benchmarks, respectively. Furthermore, experimental results show that Solid-SQL delivers an average improvement of 11.6% compared to baselines on the perturbed Spider-Syn, Spider-Realistic, and Dr. Spider benchmarks.

Graph convolutional networks (GCNs) play a critical role in improving the performance of collaborative filtering. They leverage the concept of aggregating neighbor information to capture user preferences on bipartite graphs by stacking multiple convolutional layers. However, this requirement for layer stacking often leads to a long training time for convergence, and results in indistinguishable representations with significant performance deterioration due to the problem of oversmoothing. Additionally, the noise of interactions will be amplified by the stacking of convolutional layers through message passing. To address these issues, we propose a simple, plug-and-play-Neighborhood Structure -Embedding approach, named NSE, which utilizes first-order adjacency information to construct structural embeddings. By explicitly incorporating local topologically statistical information before message passing, the embeddings propagated at GCNs have better topology-structure awareness. This leads to an improved optimization path and greater robustness against noise propagation. Experimental results demonstrate significant performance improvements by employing our proposed NSE in graph collaborative filtering models. Particularly, the NSE-enhanced LGCN shows performance gains of 5.06% and 4.86% on the Yelp and Amazon-Books datasets, respectively. The average training convergence speed is improved by 204.8%. NSE-enhanced graph collaborative filtering has also demonstrated excellent robustness against both noise and oversmoothing.

Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-target cross-domain recommendation (CDR) and are hard to be generalized to CDR with multiple target domains. In addition, the negative transfer problem is prevalent in CDR, where the recommendation performance in a target domain may not always be enhanced by knowledge learned from a source domain, especially when the source domain has sparse data. In this study, we propose CAT-ART, a multi-target CDR method that learns to improve recommendations in all participating domains through representation learning and embedding transfer. Our method consists of two parts: a self-supervised Contrastive AuToencoder (CAT) framework to generate global user embeddings based on information from all participating domains, and an Attention-based Representation Transfer (ART) framework which transfers domain-specific user embeddings from other domains to assist with target domain recommendation. CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain. We conducted extensive experiments on a collected real-world CDR dataset spanning 5 domains and involving a million users. Experimental results demonstrate the superiority of the proposed method over a range of prior arts. We further conducted ablation studies to verify the effectiveness of the proposed components. Our collected dataset will be open-sourced to facilitate future research in the field of multi-domain recommender systems and user modelling.

Cross-domain recommendation technique is a promising way to alleviate data sparsity issues by transferring knowledge from an auxiliary domain to a target domain. However, most existing works focus on utilizing the same users among different domains, while ignoring domain-specific users which forms the majority in real-world circumstances. In this paper, we propose a novel cross-domain learning approach--Relation Expansion based Cross-Domain Recommendation (ReCDR) to improve recommendation accuracies on small-overlapped domains. ReCDR first models the interactions in each domain as a local graph. It then forms a shared network by expanding out relationships using pre-trained node similarities. On the enhanced graph, ReCDR adopts a hierarchical attention mechanism. The output embedding will finally be combined with the local feature to balance the result for dual-target task. The proposed model is thoroughly evaluated on three real-world datasets. Experiments demonstrate superior performance compared to state-of-the-art methods.

Heterogeneous information networks consist of multiple types of edges and nodes, which have a strong ability to represent the rich semantics underpinning network structures. Recently, the dynamics of networks has been studied in many tasks such as social media analysis and recommender systems. However, existing methods mainly focus on the static networks or dynamic homogeneous networks, which are incapable or inefficient in modeling dynamic heterogeneous information networks. In this paper, we propose a method named Dynamic Heterogeneous Information Network Embedding (DyHINE), which can update embeddings when the network evolves. The method contains two key designs: (1) A dynamic time-series embedding module which employs a hierarchical attention mechanism to aggregate neighbor features and temporal random walks to capture dynamic interactions; (2) An online real-time updating module which efficiently updates the computed embeddings via a dynamic operator. Experiments on three real-world datasets demonstrate the effectiveness of our model compared with state-of-the-art methods on the task of temporal link prediction.