Disentangled Contrastive Collaborative Filtering

• URL: http://arxiv.org/abs/2305.02759v4
• Date: Sun, 25 Feb 2024 05:53:34 GMT
• Title: Disentangled Contrastive Collaborative Filtering
• Authors: Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin and Chao Huang
• Abstract summary: Graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue. We propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation. Our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise.
• Score: 36.400303346450514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastive learning (GCL) has exhibited powerful performance in addressing the supervision label shortage issue by learning augmented user and item representations. While many of them show their effectiveness, two key questions still remain unexplored: i) Most existing GCL-based CF models are still limited by ignoring the fact that user-item interaction behaviors are often driven by diverse latent intent factors (e.g., shopping for family party, preferred color or brand of products); ii) Their introduced non-adaptive augmentation techniques are vulnerable to noisy information, which raises concerns about the model's robustness and the risk of incorporating misleading self-supervised signals. In light of these limitations, we propose a Disentangled Contrastive Collaborative Filtering framework (DCCF) to realize intent disentanglement with self-supervised augmentation in an adaptive fashion. With the learned disentangled representations with global context, our DCCF is able to not only distill finer-grained latent factors from the entangled self-supervision signals but also alleviate the augmentation-induced noise. Finally, the cross-view contrastive learning task is introduced to enable adaptive augmentation with our parameterized interaction mask generator. Experiments on various public datasets demonstrate the superiority of our method compared to existing solutions. Our model implementation is released at the link https://github.com/HKUDS/DCCF.

Related papers

• SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies [13.26174103650211]
  A lack of explainability of learned policies impedes its uptake in safety-critical applications, such as automated driving systems. Counterfactual (CF) explanations have recently gained prominence for their ability to interpret black-box Deep Learning (DL) models. We propose using a saliency map to identify the most influential input pixels across the sequence of past observed states by the agent. We evaluate the effectiveness of our framework in diverse domains, including ADS, Atari Pong, Pacman and space-invaders games.
  arXiv  Detail & Related papers  (2024-04-28T21:47:34Z)
• Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
  Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server. Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples. We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
  arXiv  Detail & Related papers  (2023-09-14T03:48:27Z)
• Graph Masked Autoencoder for Sequential Recommendation [10.319298705782058]
  We propose a Graph Masked AutoEncoder-enhanced sequential Recommender system (MAERec) that adaptively and dynamically distills global item transitional information for self-supervised augmentation. Our method significantly outperforms state-of-the-art baseline models and can learn more accurate representations against data noise and sparsity.
  arXiv  Detail & Related papers  (2023-05-08T10:57:56Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Augmentation-induced Consistency Regularization for Classification [25.388324221293203]
  We propose a consistency regularization framework based on data augmentation, called CR-Aug. CR-Aug forces the output distributions of different sub models generated by data augmentation to be consistent with each other. We implement CR-Aug to image and audio classification tasks and conduct extensive experiments to verify its effectiveness.
  arXiv  Detail & Related papers  (2022-05-25T03:15:36Z)
• Hypergraph Contrastive Collaborative Filtering [44.8586906335262]
  We propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) HCCF captures local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. Our model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems.
  arXiv  Detail & Related papers  (2022-04-26T10:06:04Z)
• Discriminator-Free Generative Adversarial Attack [87.71852388383242]
  Agenerative-based adversarial attacks can get rid of this limitation. ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations. The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
  arXiv  Detail & Related papers  (2021-07-20T01:55:21Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [103.64435911083432]
  We present a novel contrastive learning strategy called it Proactive Pseudo-Intervention (PPI) PPI leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.
  arXiv  Detail & Related papers  (2020-12-06T20:30:26Z)
• Contextual Fusion For Adversarial Robustness [0.0]
  Deep neural networks are usually designed to process one particular information stream and susceptible to various types of adversarial perturbations. We developed a fusion model using a combination of background and foreground features extracted in parallel from Places-CNN and Imagenet-CNN. For gradient based attacks, our results show that fusion allows for significant improvements in classification without decreasing performance on unperturbed data.
  arXiv  Detail & Related papers  (2020-11-18T20:13:23Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)

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.