GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

Sankar, Aravind; Wu, Yanhong; Wu, Yuhang; Zhang, Wei; Yang, Hao; Sundaram, Hari

doi:10.48550/arxiv.2006.03736

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al [29] summarize all the stochastic augmentations on graphs and unify them into a general self-supervised graph learning framework for recommendation. Besides, there are also some studies [25,36,44] of certain members (e.g. ad hoc groups) for self-supervised recommendation.…”

Section: Self-supervised Learning In Rsmentioning

confidence: 99%

Socially-Aware Self-Supervised Tri-Training for Recommendation

Yin

Gao

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

143

View full text Add to dashboard Cite

Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based methods perturb the raw data graph with uniform node/edge dropout to generate new data views and then conduct the self-discrimination based contrastive learning over different views to learn generalizable representations. Under this scheme, only a bijective mapping is built between nodes in two different views, which means that the self-supervision signals from other nodes are being neglected. Due to the widely observed homophily in recommender systems, we argue that the supervisory signals from other nodes are also highly likely to benefit the representation learning for recommendation. To capture these signals, a general socially-aware SSL framework that integrates tri-training is proposed in this paper. Technically, our framework first augments the user data views with the user social information. And then under the regime of tri-training for multi-view encoding, the framework builds three graph encoders (one for recommendation) upon the augmented views and iteratively improves each encoder with self-supervision signals from other users, generated by the other two encoders. Since the tri-training operates on the augmented views of the same data sources for self-supervision signals, we name it self-supervised tri-training. Extensive experiments on multiple real-world datasets consistently validate the effectiveness of the self-supervised tritraining framework for improving recommendation.

show abstract

Section: Self-supervised Learning In Rsmentioning

confidence: 99%

Socially-Aware Self-Supervised Tri-Training for Recommendation

Yin

Gao

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

143

View full text Add to dashboard Cite

show abstract

“…However, these thoughts cannot be easily adopted to social recommendation where temporal factors and attributes may not be available. The most relevant work to ours is GroupIM [32], which maximizes mutual information between representations of groups and group members to overcome the sparsity problem of group interactions. As the group can be seen as a special social clique, this work can be a corroboration of the effectiveness of social selfsupervision signals.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Yin

et al. 2021

Preprint

View full text Add to dashboard Cite

Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complicated and user relations can be high-order. Hypergraph provides a natural way to model complex high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. The experimental results on multiple real-world datasets show that the proposed model outperforms the SOTA methods, and the ablation study verifies the effectiveness of the multi-channel setting and the selfsupervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.

show abstract

GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation

Cited by 2 publications

References 24 publications

Socially-Aware Self-Supervised Tri-Training for Recommendation

Socially-Aware Self-Supervised Tri-Training for Recommendation

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Contact Info

Product

Resources

About