Collaborative Filtering (CF) with implicit feedback is widely used in personalized recommender systems. In most real-world scenarios, only implicit feedback signals are available, making CF training heavily dependent on sampling-based paradigms—typically optimized via pairwise ranking losses such as Bayesian Personalized Ranking (BPR). This simple yet effective approach has achieved remarkable success and remains the foundation of many modern recommender models. However, despite its empirical success, little attention has been paid to the inherent training instability issue under this sampling-based paradigm. In this paper, we conduct an in-depth analysis of training stability and find that unstable training not only hinders convergence but also leads to fluctuating and suboptimal recommendation performance. We identify two fundamental sources of this instability in CF: (1) noisy or sparse positive samples, where a single observed interaction may not reliably reflect user preference; and (2) inconsistent negative samples, where randomly drawn negatives from unobserved space vary drastically in negative hardness, leading to uninformative or noisy gradient updates. To address these two challenges, we propose StabCF, a Stabilized Training Method for Collaborative Filtering, which improves training stability by synthesizing enriched positive samples from historical positives and constructing consistent hard negatives through user-aware negatives mixing. By replacing raw training triplets with synthesized positive-negative pairs, StabCF effectively smooths the training dynamics and improves convergence stability. Extensive experiments on three public datasets demonstrate that StabCF not only significantly stabilizes the training process but also achieves superior recommendation performance. Our PyTorch implementation is available at https://github.com/Wu-Xi/StabCF.

Vision transformers have significantly advanced the field of computer vision in recent years. The cornerstone of these transformers is the multi-head attention mechanism, which models interactions between visual elements within a feature map. However, the vanilla multi-head attention paradigm independently learns parameters for each head, which ignores crucial interactions across different attention heads and may result in redundancy and under-utilization of the model’s capacity. To enhance model expressiveness, we propose a novel nested attention mechanism, Ne-Att, that explicitly models cross-head interactions via a hierarchical variational distribution. We conducted extensive experiments on image classification, and the results demonstrate the superiority of Ne-Att.

Cross-Domain Recommendation (CDR) is capable of incorporating auxiliary information from multiple domains to advance recommendation performance. Conventional CDR methods primarily rely on overlapping users, whereby knowledge is conveyed between the source and target identities belonging to the same natural person. However, such a heuristic assumption is not universally applicable due to an individual may exhibit distinct or even conflicting preferences in different domains, leading to potential noises. In this paper, we view the anchor links between users of various domains as the learnable parameters to learn the task-relevant cross-domain correlations. A novel optimal transport based model ALCDR is further proposed to precisely infer the anchor links and deeply aggregate collaborative signals from the perspectives of intra-domain and inter-domain. Our proposal is extensively evaluated over real-world datasets, and experimental results demonstrate its superiority.

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termedHinCRec-RL, forConceptRecommendation in MOOCs, which is based onHeterogeneousInformationNetworks andReinforcementLearning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into aheterogeneous information network (HIN)to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform,XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when compared with several state-of-the-art models.