The vulnerability of graph-based recommender systems to spurious correlations has become a significant obstacle to their practical deployment, hindering their robustness in out-of-distribution (OOD) scenarios. While existing approaches offer partial solutions, they are limited by fundamental shortcomings: model-centric approaches reliant on predefined causal graphs often suffer from suboptimal performance due to complex and dynamic environmental influences. These methods typically require identifying an environmental label or performing feature decoupling, but hidden environments are often difficult to model. Furthermore, existing general feature decoupling methods fail to account for the unique structural characteristics of graphs. To overcome these challenges, we advocate for a shift towards explicit, geometrically-grounded disentanglement. Hyperbolic geometry is particularly suited for this task due to its capacity to model the inherent hierarchies of user interests. We introduce C-HyPOD : Causal Hyperbolic Representation Learning with Prototype Orthogonal Disentanglement, a novel framework designed for graph-based OOD recommendation. Unlike traditional methods, C-HyPOD transforms disentanglement into a concrete geometric task. It introduces a global interest space by learning a single set of universal interest prototypes. They provides a superior geometric foundation for ensuring these prototypes are well-separated and semantically distinct. To ensure a complete separation and prevent information leakage, a targeted orthogonality constraint is then applied. This constraint purifies the aggregated causal representation by forcing it to be orthogonal to the spurious representation in the tangent space, thereby eliminating their linear correlation. Extensive experiments on four public datasets demonstrate that C-HyPOD significantly improves OOD robustness and recommendation performance, surpassing state-of-the-art methods.

The challenges associated with large-scale user-item interaction graphs have attracted increasing attention in graph-based recommendation systems, primarily due to computational inefficiencies and inadequate information propagation. Existing methods provide partial solutions but suffer from notable limitations: model-centric approaches, such as sampling and aggregation, often struggle with generalization, while data-centric techniques, including graph sparsification and coarsening, lead to information loss and ineffective handling of bipartite graph structures. Recent advances in graph condensation offer a promising direction by reducing graph size while preserving essential information, presenting a novel approach to mitigating these challenges. Inspired by the principles of democracy, we proposeDemoRec, a framework that leverages graph condensation to generate user and item representatives for recommendation tasks. By constructing a compact interaction graph and clustering nodes with shared characteristics from the original graph, DemoRec significantly reduces graph size and computational complexity. Furthermore, it mitigates the over-reliance on high-order information, a critical challenge in large-scale bipartite graphs. Extensive experiments conducted on four public datasets demonstrate the effectiveness of DemoRec, showcasing substantial improvements in recommendation performance, computational efficiency, and robustness compared to SOTA methods.

Sequential recommendation aims to offer potentially interesting products to users by capturing their historical sequence of interacted items. Although it has facilitated extensive physical scenarios, sequential recommendation for multi-modal sequences has long been neglected. Multi-modal data that depicts a user’s historical interactions exists ubiquitously, such as product pictures, textual descriptions, and interacted item sequences, providing semantic information from multiple perspectives that comprehensively describe a user’s preferences. However, existing sequential recommendation methods either fail to directly handle multi-modality or suffer from high computational complexity. To address this, we propose a novel Multi-Modal Multi-Layer Perceptron (MMMLP) for maintaining multi-modal sequences for sequential recommendation. MMMLP is a purely MLP-based architecture that consists of three modules - the Feature Mixer Layer, Fusion Mixer Layer, and Prediction Layer - and has an edge on both efficacy and efficiency. Extensive experiments show that MMMLP achieves state-of-the-art performance with linear complexity. We also conduct ablating analysis to verify the contribution of each component. Furthermore, compatible experiments are devised, and the results show that the multi-modal representation learned by our proposed model generally benefits other recommendation models, emphasizing our model’s ability to handle multi-modal information. We have made our code available online to ease reproducibility1.