Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems

• URL: http://arxiv.org/abs/2201.03158v1
• Date: Mon, 10 Jan 2022 04:36:15 GMT
• Title: Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
• Authors: Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, Weiguo Zhang
• Abstract summary: We develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet) CRANet is capable of exploring transferable knowledge from observed and unobserved user-item interactions. We experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks.
• Score: 23.480069921831344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks. However, the majority of existing collaborative filtering systems are not well designed to handle missing data. Particularly, in order to inject the negative signals in the training phase, these solutions largely rely on negative sampling from unobserved user-item interactions and simply treating them as negative instances, which brings the recommendation performance degradation. To address the issues, we develop a Collaborative Reflection-Augmented Autoencoder Network (CRANet), that is capable of exploring transferable knowledge from observed and unobserved user-item interactions. The network architecture of CRANet is formed of an integrative structure with a reflective receptor network and an information fusion autoencoder module, which endows our recommendation framework with the ability of encoding implicit user's pairwise preference on both interacted and non-interacted items. Additionally, a parametric regularization-based tied-weight scheme is designed to perform robust joint training of the two-stage CRANet model. We finally experimentally validate CRANet on four diverse benchmark datasets corresponding to two recommendation tasks, to show that debiasing the negative signals of user-item interactions improves the performance as compared to various state-of-the-art recommendation techniques. Our source code is available at https://github.com/akaxlh/CRANet.

Related papers

• Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification [9.251342335645765]
  We propose a novel dual-stream contrastive predictive network (DCPNet) The first task is to construct positive sample pairs, guiding the core encoder to learn more general representations. The second task is to encourage adaptive capture of the correspondence between deep features and handcrated features, achieving knowledge transfer within the model, and effectively improving the redundancy caused by the feature fusion.
  arXiv  Detail & Related papers  (2023-11-26T05:47:01Z)
• 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)
• 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)
• Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach [56.12815715932561]
  We propose a new broad recommender system called Broad Collaborative Filtering (BroadCF) Instead of Deep Neural Networks (DNNs), Broad Learning System (BLS) is used as a mapping function to learn the complex nonlinear relationships between users and items. Extensive experiments conducted on seven benchmark datasets have confirmed the effectiveness of the proposed BroadCF algorithm.
  arXiv  Detail & Related papers  (2022-04-20T01:25:08Z)
• Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling [42.6279077675585]
  This work proposes a Graph Neural Multi-Behavior Enhanced Recommendation framework. It explicitly models the dependencies between different types of user-item interactions under a graph-based message passing architecture. Experiments on real-world recommendation datasets show that our GNMR consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-07T03:12:37Z)
• Latent Code-Based Fusion: A Volterra Neural Network Approach [21.25021807184103]
  We propose a deep structure encoder using the recently introduced Volterra Neural Networks (VNNs) We show that the proposed approach demonstrates a much-improved sample complexity over CNN-based auto-encoder with a superb robust classification performance.
  arXiv  Detail & Related papers  (2021-04-10T18:29:01Z)
• 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)
• BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism [106.43103176833371]
  Collaborative Filtering (CF) based recommendation methods have been widely studied. We propose a novel recommendation model named Balanced Collaborative Filtering Network (BCFNet) In addition, an attention mechanism is designed to better capture the hidden information within implicit feedback and strengthen the learning ability of the neural network.
  arXiv  Detail & Related papers  (2021-03-10T14:59:23Z)
• Local Critic Training for Model-Parallel Learning of Deep Neural Networks [94.69202357137452]
  We propose a novel model-parallel learning method, called local critic training. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We also show that trained networks by the proposed method can be used for structural optimization.
  arXiv  Detail & Related papers  (2021-02-03T09:30:45Z)
• Suppress and Balance: A Simple Gated Network for Salient Object Detection [89.88222217065858]
  We propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
  arXiv  Detail & Related papers  (2020-07-16T02:00:53Z)
• Model Fusion via Optimal Transport [64.13185244219353]
  We present a layer-wise model fusion algorithm for neural networks. We show that this can successfully yield "one-shot" knowledge transfer between neural networks trained on heterogeneous non-i.i.d. data.
  arXiv  Detail & Related papers  (2019-10-12T22:07:15Z)

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.