Self-supervised Graph Learning for Occasional Group Recommendation

• URL: http://arxiv.org/abs/2112.02274v1
• Date: Sat, 4 Dec 2021 08:10:06 GMT
• Title: Self-supervised Graph Learning for Occasional Group Recommendation
• Authors: Bowen Hao, Hongzhi Yin, Jing Zhang, Cuiping Li, and Hong Chen
• Abstract summary: We study the problem of recommending items to occasional groups (a.k.a. cold-start groups) Due to the extreme sparsity issue of the occasional groups' interactions with items, it is difficult to learn high-quality embeddings for these occasional groups. This paper proposes a self-supervised graph learning paradigm, which jointly trains the backbone GNN model to reconstruct the group/user/item embeddings.
• Score: 28.337475919795008
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of recommending items to occasional groups (a.k.a. cold-start groups), where the occasional groups are formed ad-hoc and have few or no historical interacted items. Due to the extreme sparsity issue of the occasional groups' interactions with items, it is difficult to learn high-quality embeddings for these occasional groups. Despite the recent advances on Graph Neural Networks (GNNs) incorporate high-order collaborative signals to alleviate the problem, the high-order cold-start neighbors are not explicitly considered during the graph convolution in GNNs. This paper proposes a self-supervised graph learning paradigm, which jointly trains the backbone GNN model to reconstruct the group/user/item embeddings under the meta-learning setting, such that it can directly improve the embedding quality and can be easily adapted to the new occasional groups. To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step. Besides, we add a contrastive learning (CL) adapter to explicitly consider the correlations between the group and non-group members. Experimental results on three public recommendation datasets show the superiority of our proposed model against the state-of-the-art group recommendation methods.

Related papers

• Cluster-based Graph Collaborative Filtering [55.929052969825825]
  Graph Convolution Networks (GCNs) have succeeded in learning user and item representations for recommendation systems. Most existing GCN-based methods overlook the multiple interests of users while performing high-order graph convolution. We propose a novel GCN-based recommendation model, termed Cluster-based Graph Collaborative Filtering (ClusterGCF)
  arXiv  Detail & Related papers  (2024-04-16T07:05:16Z)
• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation [58.6450834556133]
  We propose graph contrastive learning to enhance item representations with complex associations from the global view. We extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users' dynamic preferences. Our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy.
  arXiv  Detail & Related papers  (2023-03-01T05:46:36Z)
• Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph [68.08590487960475]
  We propose a novel GNN-based framework named Contextualized Factorized Attention for Group identification (CFAG) We devise tripartite graph convolution layers to aggregate information from different types of neighborhoods among users, groups, and items. To cope with the data sparsity issue, we devise a novel propagation augmentation layer, which is based on our proposed factorized attention mechanism.
  arXiv  Detail & Related papers  (2022-11-02T01:42:20Z)
• Take One Gram of Neural Features, Get Enhanced Group Robustness [23.541213868620837]
  Predictive performance of machine learning models trained with empirical risk minimization can degrade considerably under distribution shifts. We propose to partition the training dataset into groups based on Gram matrices of features extracted by an identification'' model. Our approach not only improves group robustness over ERM but also outperforms all recent baselines.
  arXiv  Detail & Related papers  (2022-08-26T12:34:55Z)
• Neural Bandit with Arm Group Graph [37.651541940052724]
  Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. We introduce a new model, Arm Group Graph (AGG), where the nodes represent the groups of arms and the weighted edges formulate the correlations among groups. To leverage the rich information in AGG, we propose a bandit algorithm, AGG-UCB, where the neural networks are designed to estimate rewards.
  arXiv  Detail & Related papers  (2022-06-08T02:16:11Z)
• Graph Representation Learning via Contrasting Cluster Assignments [57.87743170674533]
  We propose a novel unsupervised graph representation model by contrasting cluster assignments, called as GRCCA. It is motivated to make good use of local and global information synthetically through combining clustering algorithms and contrastive learning. GRCCA has strong competitiveness in most tasks.
  arXiv  Detail & Related papers  (2021-12-15T07:28:58Z)
• Graph Neural Netwrok with Interaction Pattern for Group Recommendation [1.066048003460524]
  We propose the model GIP4GR (Graph Neural Network with Interaction Pattern For Group Recommendation) Specifically, our model use the graph neural network framework with powerful representation capabilities to represent the interaction between group-user-items in the topological structure of the graph. We conducted a lot of experiments on two real-world datasets to illustrate the superior performance of our model.
  arXiv  Detail & Related papers  (2021-09-21T13:42:46Z)
• Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation [35.841350982832545]
  Group recommendation suffers seriously from the problem of data sparsity. We propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals.
  arXiv  Detail & Related papers  (2021-09-09T12:19:49Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)

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.