With the widespread adoption of collaborative filtering techniques for personalized recommendations, exposure bias has become a significant challenge.Exposure biasrefers to the tendency of recommendation models to disproportionately favor items with high exposure over those with low exposure. In graph collaborative filtering that uses graph neural networks (GNNs) for recommendations, exposure bias can be exacerbated due to 1) the reliance on positive feedback during graph construction and 2) the effects of the neighbor aggregation step in GNNs. To tackle this challenge, we propose a novel and efficient framework called FUGCF (training-Free andUnbiasedGraphCollaborativeFiltering) to improve both the accuracy and bias mitigation of graph-based personalized recommendations. FUGCF employs a two-stage calculation strategy: it estimates exposure probabilities in the first stage and then leverages them to help derive debiased node embeddings in the second stage. Furthermore, we design a training-free estimation method for FUGCF based on closed-form solutions to enhance its computational efficiency. The extensive experiments on a synthetic dataset and three real-world datasets demonstrate the effectiveness of FUGCF in reducing exposure bias, improving recommendation accuracy, and optimizing computational efficiency.

Recommender systems are increasingly prevalent to provide personalized suggestions and enhance user satisfaction. Typical recommendation models encode users and items as embeddings, and generate recommendations by assessing the similarity between these embeddings. Despite their effectiveness, these embedding-based models struggle with modeling user uncertainty and capturing diverse user interests using a single fixed user embedding. Recent studies have begun to explore a user-distribution paradigm to learn distributions for users. However, this approach employs a single distribution per user, which fails to effectively delineate semantic boundaries, resulting in sub-optimal recommendations. To this end, we propose GCDR, a Guided Conditional Diffusion Recommender model, to learn multiple distributions for each user in this paper. Specifically, GCDR addresses two major challenges: 1) learning disentangled distributions, and 2) learning personalized distributions. GCDR captures inter-user and intra-user distribution properties through conditional and guided diffusion, respectively. It maintains user-specific embeddings to encode long-term interests for conditional diffusion, while for guided diffusion, it incorporates short-term interests encoded from recent interactions with category preferences. To align the diffusion model with the recommendation task, we train GCDR with three loss functions, included the user loss, the recommendation loss and the diffusion loss. Extensive experiments on four real-world datasets show that GCDR is able to learn effective user distributions and is superior to thirteen state-of-the-art baseline methods.

Recently, several social network analysis algorithms have been optimized by leveraging the community structure commonly found in graphs. Since community structure is fun-damental to these algorithms, storing graphs based on their community structure can significantly enhance the performance of graph algorithms that rely on it for optimization. However, existing graph storage systems do not natively store graphs according to the community structure, which limits their per-formance in retrieving communities. To fill this gap, we pro-pose LSM-Community, a graph storage system inspired by the LSM - Tree design that stores graphs on disk based on their community structure. To dynamically maintain the community structure during graph updates, we present the community-centric dynamic community detection algorithm$(C^{3}D)$. Experimental results demonstrate that LSM-Community outperforms other storage systems in classical community discovery tasks (e.g., performing CD on UK-2007 dataset with LSM-Community is$86.12\times$faster than Neo4j) while maintaining high performance on classical graph analytic algorithms. This indicates that LSM-Community efficiently supports community discovery and query processing while preserving the performance of classical analytic algorithms,

The heterogeneous graph, as an effective representation of real-world data, encapsulates rich structural and semantic information. In recent years, numerous Heterogeneous Graph Neural Networks (HGNNs) have been proposed to learn node representations on heterogeneous graphs. Although existing methods have introduced various unique information aggregation and semantic fusion mechanisms, they still exhibit limitations in effectiveness and scalability. In this study, we introduce the gatekeeping theory in heterogeneous graph learning and investigate the primary challenges limiting current HGNNs. To address these challenges, we propose a novel, effective, and scalable heterogeneous graph neural network framework, the Heterogeneous Convolution-oriented Attention Network (HCAN). HCAN enhances the heterogeneous attention mechanism to learn far-sighted weights by encoding long-range relation information into node representation with a convolutional subgraph encoder. To further improve heterogeneous graph representation learning, we propose effective and scalable models based on the HCAN framework. We evaluate HCAN on various commonly used heterogeneous datasets and show that it outperforms the state-of-the-art methods, especially on challenging datasets.

Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity. Our source code and data are released at \url{https://github.com/LQgdwind/nips25-sdcgcl}.

Recent advances in graph contrastive learning (GCL) have significantly enhanced recommendation systems. However, most existing approaches predominantly focus on optimizing training data fit while overlooking exposure bias, a critical issue that can substantially impact recommendation effectiveness. Drawing inspiration from sociological theories of human interaction patterns-specifically how individuals balance self-presentation and self-hiding behaviors in social contexts-this paper proposes BPH4Rec, a novel Balancing self-Presentation and self-Hiding approach for exposure-aware Recommendation based on GCL. Within the GCL framework, BPH4Rec introduces two complementary mechanisms: (1) a self-hiding mechanism that modifies the adjacency matrix of contrastive views through custom inverse propensity scoring (IPS), effectively addressing exposure bias, and (2) a self-presentation mechanism that incorporates densification factors during matrix reconstruction to mitigate sparsity-induced biases. Through extensive evaluation on six public benchmark datasets, BPH4Rec demonstrates substantial improvements over state-of-the-art baselines, particularly in promoting long-tail item discovery while maintaining recommendation accuracy.

Procedural Language extensions of SQL (abbr. PL/SQL) enhance database programming by integrating procedural constructs with SQL's declarative syntax, thereby improving the reusability, modularity, and maintainability of SQL. Besides, PL/SQL in database systems presents significant challenges in real-world development, primarily due to the inherent complexity of programming. To reduce the development difficulty of PL/SQL, this paper studies the novel task of translating natural language (NL) to PL/SQL (i.e., NL-to-PL/SQL), aimed at simplifying PL/SQL development. Recent advancements in language models have shown promise in translating natural language questions into SQL queries (i.e., Text-to-SQL). However, the state-of-the-art Text-to-SQL methods focus only on single SQL queries, neglecting the procedural extensions of SQL, which limits their effectiveness for the NL-to-PL/SQL task. In this paper, we propose PLForge, a suite of pre-trained language models with parameter configurations of 3B, 7B, and 15B, tailored for NL-to-PL/SQL tasks. To enhance the PL/SQL generation capabilities of PLForge, we leverage a curated PL/SQL-centric data corpus and employ an incremental pre-training approach. Furthermore, to fully exploit the potential of PLForge, we propose a comprehensive prompt construction strategy tailored specifically for PL/SQL. Given the scarcity of NL-to-PL/SQL datasets, we develop a template-based method for generating NL-to-PL/SQL data. We conduct a series of experiments on PLForge and several baseline models. Based on execution match and exact match metrics that are designed specifically for the NL-to-PL/SQL task, the experimental results demonstrate that PLForge outperforms existing models in both in-context learning and supervised fine-tuning settings.

Recommender systems mitigate information overload by offering personalized suggestions to users. As the interactions between users and items can inherently be depicted as a bipartite graph, recent years have seen a surge in the interest in harnessing graph neural networks (GNNs) for enhancing recommender systems. However, a notable limitation of current GNN-based recommendation models is their exclusive emphasis on positive feedback, while disregarding the potentially insightful negative feedback. In this article, we introduce Pone-GNN, a novel recommendation model that synergistically integrates both Po sitive and ne gative feedback in G raph N eural N etworks. Pone-GNN utilizes dual embeddings–interest embeddings for capturing a user’s likes and disinterest embeddings for a user’s dislikes. Also, Pone-GNN performs message passing for both positive and negative feedback, and integrates two sets of embeddings through contrastive learning, which is crucial for extracting robust and discriminative embeddings. Our comprehensive experimental analysis demonstrates that Pone-GNN outperforms state-of-the-art models on diverse real-world recommendation datasets. For example, Pone-GNN achieves a 6.15% increase in relative nDCG@10 compared to the runner-up on the KuaiRec dataset.

Contrastive learning, a powerful self-supervised learning paradigm, has shown its efficacy in learning embed dings from independent and identically distributed (IID) as well as non-IID data without relying on label information. Since high-quality discriminative embeddings form a rich embedding space, which benefits model performance on downstream tasks, it is necessary to study how to improve the quality of contrastive node embeddings in graph contrastive learning. However, there has been limited research on this area. In this paper, we investigate how to generate high-quality contrastive node embeddings based on an in-depth analysis of graph contrastive losses. Firstly, we propose a novel and effective method, GLATE, for estimating the temperatures in three mainstream graph contrastive losses during the training phase. Secondly, we conduct the derivation of GLATE, and the derivation results reveal the specific relationship between the quality of contrastive node embeddings and tem-peratures. Finally, the extensive experiments on 16 benchmark datasets demonstrate that GLATE consistently outperforms the state-of-the-art graph contrastive learning models in terms of both model performance and training efficiency.

Synthetic graph datasets are crucial for the assessment of network analysis algorithms, providing a measure of their effectiveness and efficiency. However, most existing generation techniques typically focus on community formation, neglecting the fact that real-world communities not only emerge but may also dissipate over time. This lifecycle occurs within a finite timeframe, adding complexity to the community dynamics. In this paper, firstly we introduce the concept of time-bound community to characterize communities that are destined to disintegrate. Secondly, we devise GTB, a temporal graph Generation method featuring Time-Bound communities, leveraging newly identified patterns within real datasets that we have collected. Additionally, we propose the advanced Temporal Edge Distribution (TED) model, a key component of G TB, designed for the swift generation of temporal edges within distribution limits and offering a constant time complexity to generate each edge. The TED model not only accommodates diverse distribution configurations but also facilitates its seamless transfer between time-bound communities to enhance time and space efficiency. Finally, extensive experimental results demonstrate that our method outperforms baseline methods by delivering generated content of superior Quality with notably competitive time and space consumption.

Recommender systems have emerged as an indispensable mean to meet personalized interests of users and alleviate information overload. Despite the great success, accuracy-oriented recommendation models are creating information cocoons, i.e., it is becoming increasingly difficult for users to see other items they might be interested in. Although recent studies start paying attention to enhancing recommendation diversity, models based on point embedding fail to describe the range of user preferences and item features well, which is essential for diversified matching. To this end, we propose LCD-UC , a novel List-Check-Decide framework with UnCertainty masking based on box embedding to improve recommendation diversity with recommendation accuracy maintained. Specifically, LCD-UC creates hypercubes to represent users and items using box embedding for high model flexibility and expressiveness. Then, a hypercube similarity scoring function is designed to measure the similarity between hypercubes representing users and items. To make a balance between the accuracy and diversity of recommendations and achieve personalized diversity needs, we further develop a user-item pairwise attention mechanism as well as a user uncertainty masking mechanism in LCD-UC. Besides, we present two new metrics for better evaluation on recommendation diversity, which address the issue that existing metrics only consider the coverage of categories while ignore the frequency of categories. The extensive experiments on three real-world datasets show that LCD-UC can improve both recommendation accuracy and diversity over three base models, and is superior to six state-of-the-art recommendation models. An online 10-day AB test also demonstrates that LCD-UC can improve the performance of a real-world advertising system.

Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA.

Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks.

Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly, sparse target signals and noisy auxiliary interactions remain an issue. Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task. Hence, we propose a Multi-Behavior Self-Supervised Learning (MBSSL) framework together with an adaptive optimization method. Specifically, we devise a behavior-aware graph neural network incorporating the self-attention mechanism to capture behavior multiplicity and dependencies. To increase the robustness to data sparsity under the target behavior and noisy interactions from auxiliary behaviors, we propose a novel self-supervised learning paradigm to conduct node self-discrimination at both inter-behavior and intra-behavior levels. In addition, we develop a customized optimization strategy through hybrid manipulation on gradients to adaptively balance the self-supervised learning task and the main supervised recommendation task. Extensive experiments on five real-world datasets demonstrate the consistent improvements obtained by MBSSL over ten state-of-the-art (SOTA) baselines. We release our model implementation at: https://github.com/Scofield666/MBSSL.git.

Recently, graph neural networks have shown the superiority of modeling the complex topological structures in heterogeneous network-based recommender systems. Due to the diverse interactions among nodes and abundant semantics emerging from diverse types of nodes and edges, there is a bursting research interest in learning expressive node repre-sentations in multiplex heterogeneous networks. One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i.e., relationship). Although existing studies utilize explicit metapaths to aggregate neighbors, practically they only consider intra-relationship metapaths and thus fail to leverage the potential uplift by inter-relationship information. Moreover, it is not always straightforward to exploit inter-relationship metapaths comprehensively under diverse relationships, espe-cially with the increasing number of node and edge types. In addition, contributions of different relationships between two nodes are difficult to measure. To address the challenges, we propose HybridGNN, an end-to-end GNN model with hybrid aggregation flows and hierarchical attentions to fully utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN applies a randomized inter-relationship exploration module to exploit the multiplexity property among different relationships. Then, our model leverages hybrid aggregation flows under intra-relationship metapaths and randomized exploration to learn the rich semantics. To explore the importance of different aggregation flow and take advantage of the multiplexity property, we bring forward a novel hierarchical attention module which leverages both metapath-Ievel attention and relationship-level attention. Extensive experimental results on five real-world datasets suggest that HybridGNN achieves the best performance compared to several state-of-the-art baselines (p < 0.01, t-test) with statistical significance.