Hypergraph Contrastive Collaborative Filtering
- URL: http://arxiv.org/abs/2204.12200v2
- Date: Wed, 27 Apr 2022 20:47:50 GMT
- Title: Hypergraph Contrastive Collaborative Filtering
- Authors: Lianghao Xia and Chao Huang and Yong Xu and Jiashu Zhao and Dawei Yin
and Jimmy Xiangji Huang
- Abstract summary: 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.
- Score: 44.8586906335262
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Collaborative Filtering (CF) has emerged as fundamental paradigms for
parameterizing users and items into latent representation space, with their
correlative patterns from interaction data. Among various CF techniques, the
development of GNN-based recommender systems, e.g., PinSage and LightGCN, has
offered the state-of-the-art performance. However, two key challenges have not
been well explored in existing solutions: i) The over-smoothing effect with
deeper graph-based CF architecture, may cause the indistinguishable user
representations and degradation of recommendation results. ii) The supervision
signals (i.e., user-item interactions) are usually scarce and skewed
distributed in reality, which limits the representation power of CF paradigms.
To tackle these challenges, we propose a new self-supervised recommendation
framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly
capture local and global collaborative relations with a hypergraph-enhanced
cross-view contrastive learning architecture. In particular, the designed
hypergraph structure learning enhances the discrimination ability of GNN-based
CF paradigm, so as to comprehensively capture the complex high-order
dependencies among users. Additionally, our HCCF model effectively integrates
the hypergraph structure encoding with self-supervised learning to reinforce
the representation quality of recommender systems, based on the
hypergraph-enhanced self-discrimination. Extensive experiments on three
benchmark datasets demonstrate the superiority of our model over various
state-of-the-art recommendation methods, and the robustness against sparse user
interaction data. Our model implementation codes are available at
https://github.com/akaxlh/HCCF.
Related papers
- Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity [10.683635786183894]
CF-Diff is a new diffusion model-based collaborative filtering method.
It is capable of making full use of collaborative signals along with multi-hop neighbors.
It achieves remarkable gains up to 7.29% compared to the best competitor.
arXiv Detail & Related papers (2024-04-22T14:49:46Z) - Interactive Graph Convolutional Filtering [79.34979767405979]
Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising.
These problems are exacerbated by the cold start problem and data sparsity problem.
Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages.
Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items.
arXiv Detail & Related papers (2023-09-04T09:02:31Z) - 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) - Disentangled Contrastive Collaborative Filtering [36.400303346450514]
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.
arXiv Detail & Related papers (2023-05-04T11:53:38Z) - Ordinal Graph Gamma Belief Network for Social Recommender Systems [54.9487910312535]
We develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions.
OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences.
We extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model.
arXiv Detail & Related papers (2022-09-12T09:19:22Z) - Self-Supervised Hypergraph Transformer for Recommender Systems [25.07482350586435]
Self-Supervised Hypergraph Transformer (SHT)
Self-Supervised Hypergraph Transformer (SHT)
Cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph.
arXiv Detail & Related papers (2022-07-28T18:40:30Z) - Deep Variational Models for Collaborative Filtering-based Recommender
Systems [63.995130144110156]
Deep learning provides accurate collaborative filtering models to improve recommender system results.
Our proposed models apply the variational concept to injectity in the latent space of the deep architecture.
Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
arXiv Detail & Related papers (2021-07-27T08:59:39Z) - 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) - RGCF: Refined Graph Convolution Collaborative Filtering with concise and
expressive embedding [42.46797662323393]
We develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF)
RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive.
We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models.
arXiv Detail & Related papers (2020-07-07T12:26:10Z)
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.