Hyper-Relational Knowledge Graph Neural Network for Next POI

• URL: http://arxiv.org/abs/2311.16683v1
• Date: Tue, 28 Nov 2023 10:55:00 GMT
• Title: Hyper-Relational Knowledge Graph Neural Network for Next POI
• Authors: Jixiao Zhang, Yongkang Li, Ruotong Zou, Jingyuan Zhang, Zipei Fan, Xuan Song
• Abstract summary: Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. We propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model to exploit the rich semantics of hyper-relations.
• Score: 10.855112358613843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing works employ Knowledge Graph (KG) to alleviate the data sparsity issue in LBSN. These approaches primarily focus on modeling the pair-wise relations in LBSN to enrich the semantics and thereby relieve the data sparsity issue. However, existing approaches seldom consider the hyper-relations in LBSN, such as the mobility relation (a 3-ary relation: user-POI-time). This makes the model hard to exploit the semantics accurately. In addition, prior works overlook the rich structural information inherent in KG, which consists of higher-order relations and can further alleviate the impact of data sparsity.To this end, we propose a Hyper-Relational Knowledge Graph Neural Network (HKGNN) model. In HKGNN, a Hyper-Relational Knowledge Graph (HKG) that models the LBSN data is constructed to maintain and exploit the rich semantics of hyper-relations. Then we proposed a Hypergraph Neural Network to utilize the structural information of HKG in a cohesive way. In addition, a self-attention network is used to leverage sequential information and make personalized recommendations. Furthermore, side information, essential in reducing data sparsity by providing background knowledge of POIs, is not fully utilized in current methods. In light of this, we extended the current dataset with available side information to further lessen the impact of data sparsity. Results of experiments on four real-world LBSN datasets demonstrate the effectiveness of our approach compared to existing state-of-the-art methods.

Related papers

• RelGNN: Composite Message Passing for Relational Deep Learning [56.48834369525997]
  We introduce RelGNN, a novel GNN framework specifically designed to capture the unique characteristics of relational databases. At the core of our approach is the introduction of atomic routes, which are sequences of nodes forming high-order tripartite structures. RelGNN consistently achieves state-of-the-art accuracy with up to 25% improvement.
  arXiv  Detail & Related papers  (2025-02-10T18:58:40Z)
• Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy [6.295242666794106]
  FedHGL is designed to collaboratively train a comprehensive hypergraph neural network across multiple clients. Cross-client feature aggregation is performed and distributed at the central server to ensure that this information can be utilized by the clients.
  arXiv  Detail & Related papers  (2024-08-09T16:31:41Z)
• Redundancy-Free Self-Supervised Relational Learning for Graph Clustering [13.176413653235311]
  We propose a novel self-supervised deep graph clustering method named Redundancy-Free Graph Clustering (R$2$FGC) It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. Our experiments are performed on widely used benchmark datasets to validate the superiority of our R$2$FGC over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-09T06:18:50Z)
• Distributed Learning over Networks with Graph-Attention-Based Personalization [49.90052709285814]
  We propose a graph-based personalized algorithm (GATTA) for distributed deep learning. In particular, the personalized model in each agent is composed of a global part and a node-specific part. By treating each agent as one node in a graph the node-specific parameters as its features, the benefits of the graph attention mechanism can be inherited.
  arXiv  Detail & Related papers  (2023-05-22T13:48:30Z)
• Equivariant Hypergraph Diffusion Neural Operators [81.32770440890303]
  Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data. This work proposes a new HNN architecture named ED-HNN, which provably represents any continuous equivariant hypergraph diffusion operators. We evaluate ED-HNN for node classification on nine real-world hypergraph datasets.
  arXiv  Detail & Related papers  (2022-07-14T06:17:00Z)
• Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System [9.665475078766017]
  We propose an innovative relationship prediction method, DeepWORD, by designing a graph convolutional network (GCN) In the experiments we learned that the proposed method achieved state-of-the-art accuracy and real-time performance.
  arXiv  Detail & Related papers  (2022-01-28T13:12:27Z)
• Learning Intents behind Interactions with Knowledge Graph for Recommendation [93.08709357435991]
  Knowledge graph (KG) plays an increasingly important role in recommender systems. Existing GNN-based models fail to identify user-item relation at a fine-grained level of intents. We propose a new model, Knowledge Graph-based Intent Network (KGIN)
  arXiv  Detail & Related papers  (2021-02-14T03:21:36Z)
• A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information [16.73333758538986]
  We exploit the relevant and easily accessible textual information by advanced natural language processing (NLP) models. We propose a light RGCN-based (RGCN, relational graph convolutional network) collaborative filtering method on heterogeneous graphs.
  arXiv  Detail & Related papers  (2020-10-04T11:10:37Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)
• Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs [96.73259297063619]
  We consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions. We leverage Generative Adrial Networks (GANs) to establish the connection between text and knowledge graph domain.
  arXiv  Detail & Related papers  (2020-01-08T01:19:08Z)

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.