Causal Discovery in Recommender Systems: Example and Discussion

• URL: http://arxiv.org/abs/2409.10271v1
• Date: Mon, 16 Sep 2024 13:31:04 GMT
• Title: Causal Discovery in Recommender Systems: Example and Discussion
• Authors: Emanuele Cavenaghi, Fabio Stella, Markus Zanker,
• Abstract summary: Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs.
• Score: 3.013819656993265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal discovery task to learn a causal graph by combining observational data from an open-source dataset with prior knowledge. The resulting causal graph shows that only a few variables effectively influence the analysed feedback signals. This contrasts with the recent trend in the machine learning community to include more and more variables in massive models, such as neural networks.

Related papers

• A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation [59.14165404728197]
  We provide an up-to-date and forward-looking review of deep graph learning under distribution shifts. Specifically, we cover three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. To provide a better understanding of the literature, we systematically categorize the existing models based on our proposed taxonomy.
  arXiv  Detail & Related papers  (2024-10-25T02:39:56Z)
• Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction [1.9116784879310031]
  A morphological brain graph depicting a connectional fingerprint is of paramount importance for charting brain dysconnectivity patterns. We propose the Graph Residual Noise Learner Network (Grenol-Net), the first graph diffusion model for predicting a target graph from a source graph.
  arXiv  Detail & Related papers  (2024-09-30T17:28:38Z)
• Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models [39.33024157496401]
  We introduce GODM, a novel data augmentation for mitigating class imbalance in supervised graph outlier detection with latent Diffusion Models. Our proposed method consists of three key components: (1) Variantioanl maps the heterogeneous information inherent within the graph data into a unified latent space, (2) Graph Generator synthesizes graph data that are statistically similar to real outliers from latent space, and (3) Latent Diffusion Model learns the latent space distribution of real organic data by iterative denoising.
  arXiv  Detail & Related papers  (2023-12-29T16:50:40Z)
• NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics [10.294767093317404]
  We introduce NeuroGraph, a collection of graph-based neuroimaging datasets. We demonstrate its utility for predicting multiple categories of behavioral and cognitive traits.
  arXiv  Detail & Related papers  (2023-06-09T19:10:16Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Robust Causal Graph Representation Learning against Confounding Effects [21.380907101361643]
  We propose Robust Causal Graph Representation Learning (RCGRL) to learn robust graph representations against confounding effects. RCGRL introduces an active approach to generate instrumental variables under unconditional moment restrictions, which empowers the graph representation learning model to eliminate confounders.
  arXiv  Detail & Related papers  (2022-08-18T01:31:25Z)
• Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices) [10.616061367794385]
  We propose a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs. We show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer.
  arXiv  Detail & Related papers  (2022-05-19T18:21:12Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data [63.15776078733762]
  We propose Amortized Causal Discovery, a novel framework to learn to infer causal relations from time-series data. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance.
  arXiv  Detail & Related papers  (2020-06-18T19:59:12Z)

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.