Graph Contrastive Learning (GCL) emerges as a powerful approach in recommendation systems, leveraging graph structures to learn effective representations. However, existing contrastive sampling strategies often introduce unintended biases, most notably, the misclassification of genuine positive samples as negatives, which undermines representation quality and overall recommendation performance. Accordingly, this paper revisits the conventional contrastive sampling and introduces Negative-Free Sampling for Graph Contrastive Learning (NFS). NFS adopts a two-stage sampling strategy that selectively identifies and utilizes only positive instances during training. By removing reliance on negative samples, it effectively mitigates misclassification bias and improves the semantic alignment between related representations. In addition, a comprehensive theoretical analysis is also provided to establish the robustness of NFS against representation collapse. Experimental results on three benchmarks demonstrate that NFS consistently outperforms or performs state-of-the-art methods, achieving up to a 14.2% relative improvement across evaluated datasets. In addition, a detailed ablation study is also provided to examine how exclusively leveraging positive samples contributes to the efficiency of GCL. The results further demonstrate the plug-and-play nature of the proposed method and its resilience to noisy data.

Adversarial examples pose significant challenges for Natural Language Processing (NLP) model robustness, often causing notable performance degradation. While various detection methods have been proposed with the aim of differentiating clean and adversarial inputs, they often require fine-tuning with ample data, which is problematic for low-resource scenarios. To alleviate this issue, a Subspace Clustering based Adversarial Detector (termed SCAD) is proposed in this paper, leveraging a union of subspaces to model the clean data distribution. Specifically, SCAD estimates feature distribution across semantic subspaces, assigning unseen examples to the nearest one for effective discrimination. The construction of semantic subspaces does not require many observations and hence ideal for the low-resource setting.

Ad hoc table retrieval refers to the task of performing semantic matching between given queries and candidate tables. In recent years, the approach to addressing this retrieval task has undergone significant shifts, transitioning from utilizing hand-crafted features to leveraging the power of Pre-trained Language Models (PLMs). However, key challenges arise when candidate tables contain shared items, and/or queries may refer to only a subset of table items rather than the entire one. Existing models often struggle to distinguish the most informative items and fail to accurately identify the relevant items required to match with the query.

Sequential recommendation is of great importance for a variety of purposes, such as application engineering, resource optimization, and marketing. Yet, existing sequence-based recommendation models are susceptible to adversarial attacks, which aim to perturb input sequences and mislead trained models, resulting in incorrect predictions. Defense methods are accordingly adopted to enhance model robustness. Nevertheless, these methods encounter challenges, such as error propagation (from the model output to generate adversarial samples), the high system complexity, and the difficulty of maintaining the model generalizability. To bridge this gap, this paper introduces a simple yet effective adversarial defense algorithm, termed Perturbation-Driven Sequential Recommendation (PDSR). In the training process, PDSR leverages a simple perturbation-generation module to create adversarial samples, eliminating the need for gradient estimation, thus streamlining the process. Additionally, it also incorporates a robust encoder designed to increase tolerance towards representation variations by ensuring alignment between original and perturbed representations, thereby boosting model generalizability. Comprehensive experiments are conducted based on a combination of five benchmark datasets, two attack methods, and four sequential recommendation models. When compared to four state-of-the-art defense baselines, PDSR demonstrates notable improvements in defense performance.