Bi-Level Graph Structure Learning for Next POI Recommendation

• URL: http://arxiv.org/abs/2411.01169v1
• Date: Sat, 02 Nov 2024 07:40:16 GMT
• Title: Bi-Level Graph Structure Learning for Next POI Recommendation
• Authors: Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang,
• Abstract summary: Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. This paper presents a novel Bi-level Graph Structure Learning (BiGSL) for next POI recommendation.
• Score: 28.44264733067864
• License:
• Abstract: Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs) have demonstrated a remarkable capability in this endeavor by exploiting the extensive global collaborative signals present among POIs. However, most of the existing graph-based approaches construct graph structures based on pre-defined heuristics, failing to consider inherent hierarchical structures of POI features such as geographical locations and visiting peaks, or suffering from noisy and incomplete structures in graphs. To address the aforementioned issues, this paper presents a novel Bi-level Graph Structure Learning (BiGSL) for next POI recommendation. BiGSL first learns a hierarchical graph structure to capture the fine-to-coarse connectivity between POIs and prototypes, and then uses a pairwise learning module to dynamically infer relationships between POI pairs and prototype pairs. Based on the learned bi-level graphs, our model then employs a multi-relational graph network that considers both POI- and prototype-level neighbors, resulting in improved POI representations. Our bi-level structure learning scheme is more robust to data noise and incompleteness, and improves the exploration ability for recommendation by alleviating sparsity issues. Experimental results on three real-world datasets demonstrate the superiority of our model over existing state-of-the-art methods, with a significant improvement in recommendation accuracy and exploration performance.

Related papers

• A Pure Transformer Pretraining Framework on Text-attributed Graphs [50.833130854272774]
  We introduce a feature-centric pretraining perspective by treating graph structure as a prior. Our framework, Graph Sequence Pretraining with Transformer (GSPT), samples node contexts through random walks. GSPT can be easily adapted to both node classification and link prediction, demonstrating promising empirical success on various datasets.
  arXiv  Detail & Related papers  (2024-06-19T22:30:08Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Homophily-enhanced Structure Learning for Graph Clustering [19.586401211161846]
  Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. Previous endeavors in graph structure learning have predominantly centered around supervised settings. We propose a novel method called textbfhomophily-enhanced structure textbflearning for graph clustering (HoLe)
  arXiv  Detail & Related papers  (2023-08-10T02:53:30Z)
• Strengthening structural baselines for graph classification using Local Topological Profile [0.0]
  We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. We propose a new baseline algorithm called Local Topological Profile (adam), which extends LDP by using additional centrality measures and local descriptors.
  arXiv  Detail & Related papers  (2023-05-01T08:59:58Z)
• Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification [5.000404730573809]
  Graph Neural Networks (GNNs) have attracted increasing attention in recent years. Recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph. We propose a novel framework for semi-supervised classification.
  arXiv  Detail & Related papers  (2022-12-04T06:17:11Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Kernel-based Substructure Exploration for Next POI Recommendation [20.799741790823425]
  Point-of-Interest (POI) recommendation plays an increasingly important role in recommender systems. Most existing methods usually merely leverage recurrent neural networks (RNNs) to explore sequential influences for recommendation. We propose a Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which combines the characteristics of both geographical and sequential influences.
  arXiv  Detail & Related papers  (2022-10-08T08:36:34Z)
• Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions [123.07967420310796]
  This paper bridges the gap by proposing a pairwise framework for noisy node classification on graphs. PI-GNN relies on the PI as a primary learning proxy in addition to the pointwise learning from the noisy node class labels. Our proposed framework PI-GNN contributes two novel components: (1) a confidence-aware PI estimation model that adaptively estimates the PI labels, and (2) a decoupled training approach that leverages the estimated PI labels.
  arXiv  Detail & Related papers  (2021-06-14T14:23:08Z)
• Graph Information Bottleneck [77.21967740646784]
  Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Inheriting from the general Information Bottleneck (IB), GIB aims to learn the minimal sufficient representation for a given task. We show that our proposed models are more robust than state-of-the-art graph defense models.
  arXiv  Detail & Related papers  (2020-10-24T07:13:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.