Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Yu, Junliang; Gao, Min; Yin, Hongzhi; Li, Jundong; Gao, Chongming; Wang, Qinyong

doi:10.1109/icdm.2019.00087

Cited by 74 publications

(33 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The common ideas of MF-based social recommendation algorithms can be categorized into three groups: co-factorization methods [22,46], ensemble methods [20], and regularization methods [23]. Besides, there are also studies using socially-aware MF to model point-of-interest [48,51,52], preference evolution [39], item ranking [55,61], and relation generation [11,57]. Over the recent years, the boom of deep learning has broadened the ways to explore social recommendation.…”

Section: Related Work 21 Social Recommendationmentioning

confidence: 99%

“…Generally, in a social recommender system, if a user has few interactions with items, the system would rely on her friends' interactions to infer her preference and generate better recommendations. Upon this paradigm, a large number of social recommendation models have been developed [12,21,23,55,57,61] and have shown stronger performance compared with general recommendation models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yin

et al. 2021

Proceedings of the Web Conference 2021

Self Cite

239

119

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

Section: Related Work 21 Social Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yin

et al. 2021

Proceedings of the Web Conference 2021

Self Cite

239

119

View full text Add to dashboard Cite

show abstract

“…RSGAN [14]: It is a state-of-the-art graph convolutional network algorithm, which generates reliable friends by adversarial generative networks to alleviate the sparse problem of trust data and improve recommendation performance.…”

Section: ) Baselinesmentioning

confidence: 99%

“…Intuitively, this will help the recommendation system to predict user preferences, but the noise reduction makes trust information more sparse, and the trust information with high sparsity can not improve the recommendation performance effectively. In the meantime, some works [12], [13], [48] attempt to alleviate the sparse problem by generating more trust information, for example, Yu et al [14] propose a Generative Adversarial Nets(GAN) to generate social information, GAN consists of two parts: a generator which is used to generate users trust friends; a discriminator which is used to discriminate whether the generated friends similar to user's real friends. Generate friends by the algorithm to alleviate the problem of sparse data, the following problem is, if GAN selects noisy trust data, it takes the model in the wrong direction.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

Seng

Lai

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In the existing social recommendation algorithms, the performance of the algorithms is bothered by the problem of noise and sparsity in social information. In the past few years, researchers have usually focused on solving noise problem or sparse problem, but ignored a key problem, that is if only solving the noise problem, the sparse problem will become more serious, similarly, if only solving the sparse problem, the noise problem will become more serious. Recently, a few studies attempt to solve both noise and sparse problems, usually using explicit trust features to filter trust information and generate implicit trust friends, but this will depend heavily on the quality of explicit trust features. Therefore, in this paper, we propose an adaptive algorithm based on graph convolution network, The main idea is to learn users' implicit trust behavior adaptively. Briefly, we use Jaccard similarity coefficient to filter out unnecessary trust information, the reliable trust information will be the users' trust attributes, then put into the graph convolution network, because of the good feature extraction ability of graph convolution network, we can use adaptive network to learn implicit trust features based on users' features, the trust features can infer the trust attributes for all users. In order to achieve the best effect of learning, we use iterative repetition of the above process, to mining the implicit influence of user trust behavior, so as to alleviate noise and sparsity problems. Experimental result shows that, on three open datasets: LastFM, Douban and Gowalla, the recommendation performance is improved, which proves the effectiveness of the algorithm.

show abstract

“…GraphGAN [122] 2018 ✓ ✓ ✓ GAN-HBNR [11] 2018 ✓ ✓ ✓ VCGAN [145] 2018 ✓ ✓ ✓ UPGAN [48] 2020 ✓ ✓ ✓ Hybrid Collaborative Rec. VAE-AR [66] 2017 ✓ ✓ ✓ RGD-TR [71] 2018 ✓ ✓ ✓ aae-RS [136] 2018 ✓ ✓ ✓ SDNet [26] 2019 ✓ ✓ ✓ ATR [89] 2019 ✓ ✓ ✓ AugCF [127] 2019 ✓ ✓ ✓ RSGAN [138] 2019 ✓ ✓ ✓ RRGAN [24] 2019 ✓ ✓ ✓ UGAN [129] 2019 ✓ ✓ ✓ LARA [107] 2020 ✓ ✓ ✓ CGAN [28] 2020 ✓ ✓ ✓ Context-aware Rec. Temporal-aware RecGAN [8] 2018 ✓ ✓ ✓ NMRN-GAN [126] 2018 ✓ ✓ ✓ AAE [116] 2018 ✓ ✓ ✓ PLASTIC [147] 2018 [25] 2019 ✓ ✓ ✓ Geographical-aware Geo-ALM [75] 2019 ✓ ✓ ✓ APOIR [148] 2019 ✓ ✓ ✓ Cross-domain Rec.…”

Section: Model Namementioning

confidence: 99%

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Deldjoo

Noia

Merra

2020

Proceedings of the 13th International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to their excellent performance and recommendation accuracy. Notwithstanding their great success, in recent years, it has been shown that these methods are vulnerable to adversarial examples, i.e., subtle but non-random perturbations designed to force recommendation models to produce erroneous outputs. The main reason for this behavior is that user interaction data used for training of LFM can be contaminated by malicious activities or users' misoperation that can induce an unpredictable amount of natural noise and harm recommendation outcomes. On the other side, it has been shown that these systems, conceived originally to attack machine learning applications, can be successfully adopted to strengthen their robustness against attacks as well as to train more precise recommendation engines.In this respect, the goal of this survey is two-fold: (i) to present recent advances on AML-RS for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs), which use the core concept of learning in AML (i.e., the min-max game) for generative applications.In this survey, we provide an exhaustive literature review of 60 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS and recommendation models leveraging generative models to improve their quality.

show abstract

Generating Reliable Friends via Adversarial Training to Improve Social Recommendation

Cited by 74 publications

References 43 publications

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

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

Adaptive Learning User Implicit Trust Behavior Based on Graph Convolution Network

Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Contact Info

Product

Resources

About