GLIMG: Global and Local Item Graphs for Top-N Recommender Systems

• URL: http://arxiv.org/abs/2007.14018v3
• Date: Wed, 11 Aug 2021 15:29:05 GMT
• Title: GLIMG: Global and Local Item Graphs for Top-N Recommender Systems
• Authors: Zhuoyi Lin, Lei Feng, Rui Yin, Chi Xu, and Chee-Keong Kwoh
• Abstract summary: We propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs) By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.
• Score: 12.631785780195996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based recommendation models work well for top-N recommender systems due to their capability to capture the potential relationships between entities. However, most of the existing methods only construct a single global item graph shared by all the users and regrettably ignore the diverse tastes between different user groups. Inspired by the success of local models for recommendation, this paper provides the first attempt to investigate multiple local item graphs along with a global item graph for graph-based recommendation models. We argue that recommendation on global and local graphs outperforms that on a single global graph or multiple local graphs. Specifically, we propose a novel graph-based recommendation model named GLIMG (Global and Local IteM Graphs), which simultaneously captures both the global and local user tastes. By integrating the global and local graphs into an adapted semi-supervised learning model, users' preferences on items are propagated globally and locally. Extensive experimental results on real-world datasets show that our proposed method consistently outperforms the state-of-the art counterparts on the top-N recommendation task.

Related papers

• Toward Personalized Federated Node Classification in One-shot Communication [27.325478113745206]
  We propose a one-shot personalized Federated Graph Learning method for node classification. Our method estimates and aggregates class-wise feature distribution statistics to construct a global pseudo-graph on the server. Our method significantly outperforms state-of-the-art baselines across various settings.
  arXiv  Detail & Related papers  (2024-11-18T05:59:29Z)
• MGNet: Learning Correspondences via Multiple Graphs [78.0117352211091]
  Learning correspondences aims to find correct correspondences from the initial correspondence set with an uneven correspondence distribution and a low inlier rate. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or stack local graphs into the global one to complete the task. We propose MGNet to effectively combine multiple complementary graphs.
  arXiv  Detail & Related papers  (2024-01-10T07:58:44Z)
• APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation [86.29366168836141]
  We propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR) APGL4SR incorporates adaptive and personalized global collaborative information into sequential recommendation systems. As a generic framework, APGL4SR can outperform other baselines with significant margins.
  arXiv  Detail & Related papers  (2023-11-06T01:33:24Z)
• 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)
• GraphWorld: Fake Graphs Bring Real Insights for GNNs [4.856486822139849]
  GraphWorld allows a user to efficiently generate a world with millions of statistically diverse datasets. We present insights from GraphWorld experiments regarding the performance characteristics of tens of thousands of GNN models over millions of benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-28T22:00:02Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• Node-wise Localization of Graph Neural Networks [52.04194209002702]
  Graph neural networks (GNNs) emerge as a powerful family of representation learning models on graphs. We propose a node-wise localization of GNNs by accounting for both global and local aspects of the graph. We conduct extensive experiments on four benchmark graphs, and consistently obtain promising performance surpassing the state-of-the-art GNNs.
  arXiv  Detail & Related papers  (2021-10-27T10:02:03Z)
• Self-supervised Graph-level Representation Learning with Local and Global Structure [71.45196938842608]
  We propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model.
  arXiv  Detail & Related papers  (2021-06-08T05:25:38Z)
• FedGL: Federated Graph Learning Framework with Global Self-Supervision [22.124339267195822]
  FedGL is capable of obtaining a high-quality global graph model while protecting data privacy. The global self-supervision enables the information of each client to flow and share in a privacy-preserving manner.
  arXiv  Detail & Related papers  (2021-05-07T11:27:23Z)
• Bipartite Graph Embedding via Mutual Information Maximization [8.382665371140503]
  Bipartite graph embedding has attracted much attention due to the fact that bipartite graphs are widely used in various application domains. We propose a bipartite graph embedding called BiGI to capture such global properties by introducing a novel local-global infomax objective. Our model is evaluated on various benchmark datasets for the tasks of top-K recommendation and link prediction.
  arXiv  Detail & Related papers  (2020-12-10T04:03:39Z)

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.