On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering

• URL: http://arxiv.org/abs/2306.03624v1
• Date: Tue, 6 Jun 2023 12:22:09 GMT
• Title: On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
• Authors: Jiayan Guo and Lun Du and Xu Chen and Xiaojun Ma and Qiang Fu and Shi Han and Dongmei Zhang and Yan Zhang
• Abstract summary: Graph Collaborative (CF) is an important research direction in recommender systems. In this paper, we analyze the important factors that a graph filter should consider to achieve better performance. Based on Jacobi bases and filtering frequency decomposition strategies, we design JGCF, which brings at most 27.06% performance gain on Alibaba-iFashion.
• Score: 45.75671148689973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Collaborative filtering (CF) is an important research direction in recommender systems that aims to make recommendations given the information on user-item interactions. Graph CF has attracted more and more attention in recent years due to its effectiveness in leveraging high-order information in the user-item bipartite graph for better recommendations. Specifically, recent studies show the success of graph neural networks (GNN) for CF is attributed to its low-pass filtering effects. However, current researches lack a study of how different signal components contributes to recommendations, and how to design strategies to properly use them well. To this end, from the view of spectral transformation, we analyze the important factors that a graph filter should consider to achieve better performance. Based on the discoveries, we design JGCF, an efficient and effective method for CF based on Jacobi polynomial bases and frequency decomposition strategies. Extensive experiments on four widely used public datasets show the effectiveness and efficiency of the proposed methods, which brings at most 27.06% performance gain on Alibaba-iFashion. Besides, the experimental results also show that JGCF is better at handling sparse datasets, which shows potential in making recommendations for cold-start users.

Related papers

• Amplify Graph Learning for Recommendation via Sparsity Completion [16.32861024767423]
  Graph learning models have been widely deployed in collaborative filtering (CF) based recommendation systems. Due to the issue of data sparsity, the graph structure of the original input lacks potential positive preference edges. We propose an Amplify Graph Learning framework based on Sparsity Completion (called AGL-SC)
  arXiv  Detail & Related papers  (2024-06-27T08:26:20Z)
• How Does Message Passing Improve Collaborative Filtering? [49.019075781827034]
  Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. Message passing helps CF methods in a manner akin to its benefits for graph-based learning tasks in general. We present Test-time Aggregation for CF, a test-time augmentation framework that only conducts message passing once at inference time.
  arXiv  Detail & Related papers  (2024-03-27T18:53:04Z)
• Adaptive spectral graph wavelets for collaborative filtering [5.547800834335382]
  Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions. We introduce a spectral graph wavelet collaborative filtering framework for implicit feedback data, where users, items and their interactions are represented as a bipartite graph. In addition to capturing the graph's local and global structures, our approach yields localization of graph signals in both spatial and spectral domains.
  arXiv  Detail & Related papers  (2023-12-05T22:22:25Z)
• Neural Graph Collaborative Filtering Using Variational Inference [19.80976833118502]
  We introduce variational embedding collaborative filtering (GVECF) as a novel framework to incorporate representations learned through a variational graph auto-encoder. Our proposed method achieves up to 13.78% improvement in the recall over the test data.
  arXiv  Detail & Related papers  (2023-11-20T15:01:33Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation [56.10351068286499]
  We propose Learnable Data Augmentation for Graph Contrastive Learning (LDA-GCL) Our methods include data augmentation learning and graph contrastive learning, which follow the InfoMin and InfoMax principles, respectively. In implementation, our methods optimize the adversarial loss function to learn data augmentation and effective representations of users and items.
  arXiv  Detail & Related papers  (2023-02-05T06:55:51Z)
• An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering [79.17319280791237]
  We propose an adaptive graph pre-training framework for localized collaborative filtering (ADAPT) ADAPT captures both the common knowledge across different graphs and the uniqueness for each graph. It does not require transferring user/item embeddings, and is able to capture both the common knowledge across different graphs and the uniqueness for each graph.
  arXiv  Detail & Related papers  (2021-12-14T06:53:13Z)
• How Powerful is Graph Convolution for Recommendation? [21.850817998277158]
  Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF) In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing.
  arXiv  Detail & Related papers  (2021-08-17T11:38:18Z)
• Localized Graph Collaborative Filtering [20.868562372148677]
  We introduce a novel perspective to build GNN-based CF methods for recommendations. One key advantage of LGCF is that it does not need to learn embeddings for each user and item. Experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios.
  arXiv  Detail & Related papers  (2021-08-10T06:48:32Z)
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [55.44107800525776]
  Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models. In this paper, we revisit GCN based Collaborative Filtering (CF) based Recommender Systems (RS) We show that removing non-linearities would enhance recommendation performance, consistent with the theories in simple graph convolutional networks. We propose a residual network structure that is specifically designed for CF with user-item interaction modeling.
  arXiv  Detail & Related papers  (2020-01-28T04:41:25Z)

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.