Hao Wu

Junfeng Long, Hao Wu

Graph Neural Networks (GNNs) are powerful learning methods for recommender systems owing to their robustness in handling complicated user-item interactions. Recently, the integration of contrastive learning with GNNs has demonstrated remarkable performance in recommender systems to handle the issue of highly sparse user-item interaction data. Yet, some available graph contrastive learning (GCL) techniques employ stochastic augmentation, i.e., nodes or edges are randomly perturbed on the user-item bipartite graph to construct contrastive views. Such a stochastic augmentation strategy not only brings noise perturbation but also cannot utilize global collaborative signals effectively. To address it, this study proposes a latent factor analysis (LFA) enhanced GCL approach, named LFA-GCL. Our model exclusively incorporates LFA to implement the unconstrained structural refinement, thereby obtaining an augmented global collaborative graph accurately without introducing noise signals. Experiments on four public datasets show that the proposed LFA-GCL outperforms the state-of-the-art models.

Bo Tang, Junyi Zhu, Chenyang Xi et al.

Traditional search engines struggle to synthesize fragmented information for complex queries, while generative AI search engines face challenges in relevance, comprehensiveness, and presentation. To address these limitations, we introduce Xinyu AI Search, a novel system that incorporates a query-decomposition graph to dynamically break down complex queries into sub-queries, enabling stepwise retrieval and generation. Our retrieval pipeline enhances diversity through multi-source aggregation and query expansion, while filtering and re-ranking strategies optimize passage relevance. Additionally, Xinyu AI Search introduces a novel approach for fine-grained, precise built-in citation and innovates in result presentation by integrating timeline visualization and textual-visual choreography. Evaluated on recent real-world queries, Xinyu AI Search outperforms eight existing technologies in human assessments, excelling in relevance, comprehensiveness, and insightfulness. Ablation studies validate the necessity of its key sub-modules. Our work presents the first comprehensive framework for generative AI search engines, bridging retrieval, generation, and user-centric presentation.