Self-Supervised Learning for Recommender Systems: A Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec (https://github.com/Coder-Yu/SELFRec), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.Comment: 20 pages. Accepted by TKD

MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges

MiR-592 Promotes Gastric Cancer Proliferation, Migration, and Invasion Through the PI3K/AKT and MAPK/ERK Signaling Pathways by Targeting Spry2

Background/Aims: Gastric cancer (GC) is one of the most prevalent digestive malignancies. MicroRNAs (miRNAs) are involved in multiple cellular processes, including oncogenesis, and miR-592 itself participates in many malignancies; however, its role in GC remains unknown. In this study, we investigated the expression and molecular mechanisms of miR-592 in GC. Methods: Quantitative real-time PCR and immunohistochemistry were performed to determine the expression of miR-592 and its putative targets in human tissues and cell lines. Proliferation, migration, and invasion were evaluated by Cell Counting Kit-8, population doubling time, colony formation, Transwell, and wound-healing assays in transfected GC cells in vitro. A dual-luciferase reporter assay was used to determine whether miR-592 could directly bind its target. A tumorigenesis assay was used to study whether miR-592 affected GC growth in vivo. Proteins involved in signaling pathways and the epithelial–mesenchymal transition (EMT) were detected with western blot. Results: The ectopic expression of miR-592 promoted GC proliferation, migration, and invasion in vitro and facilitated tumorigenesis in vivo. Spry2 was a direct target of miR-592 and Spry2 overexpression partially counteracted the effects of miR-592. miR-592 induced the EMT and promoted its progression in GC via the PI3K/AKT and MAPK/ERK signaling pathways by inhibiting Spry2. Conclusions: Overexpression of miR-592 promotes GC proliferation, migration, and invasion and induces the EMT via the PI3K/AKT and MAPK/ERK signaling pathways by inhibiting Spry2, suggesting a potential therapeutic target for GC

Modeling and Simulation of Opinion Natural Reversal Dynamics with Opinion Leader Based on HK Bounded Confidence Model

Opinion natural reversals are important and common phenomena in network management. It is a naturally emerging process of opinions characterized by interactions between individuals and the evolution of attitudes themselves. To explore the underlying mechanism of this social phenomenon and to reveal its dynamic traits, we propose here a novel model which takes the effects of natural reversal parameter and opinion interaction on the individual’s view choice behavior into account based on the Hegselmann and Krause (HK) bounded confidence model. Experimental results show that the evolution of individual opinions is not only influenced by the interactions between neighboring individuals but also updated naturally due to individual factors themselves in the absence of interaction, which in turn proves that the proposed model can provide a reasonable description of the entire process of public opinion natural reversal under the Internet environment. Besides, the proportion of group opinion tendency, network topology, identification method, and the influence weight of opinion leader will play significant roles in this process, which further indicates our improved model is very robust and thus can provide some insightful evidence to understand the phenomena of opinion natural reversal

Structure Reversal of Online Public Opinion for the Heterogeneous Health Concerns under NIMBY Conflict Environmental Mass Events in China

Public opinions play an important role in the formation of Not in My Back Yard (NIMBY) conflict environmental mass events. Due to the continual interactions between affected groups and the corresponding government responses surrounding the public interests related to health, online public opinion structure reversal arises frequently in NIMBY conflict events, which pose a serious threat to social public security. To explore the underlying mechanism, this paper introduces an improved dynamic model which considers multiple heterogeneities in health concerns and social power of individuals and in government’s ability. The experimental results indicate that the proposed model can provide an accurate description of the entire process of online public opinion structure reversal in NIMBY conflict environmental mass incidents on the Internet. In particular, the proportion of the individual agents without health interest appeals will delay the online public opinion structure reversal, and the upper threshold remains within regulatory limits from 0.4 to 0.5. Unlike some previous results that show that the guiding powers of the opinion leaders varied over its ratio in a fixed-sized group, our results suggest that the impact of opinion leaders is of no significant difference for the time of structure reversal after it increased to about 6%. Furthermore, a double threshold effect of online structure reversal during the government’s response process was observed. The findings are beneficial for understanding and explaining the process of online public opinion structure reversal in NIMBY conflict environmental mass incidents, and provides theoretical and practical implications for guiding public or personal health opinions on the Internet and for a governments’ effective response to them

Adaptive implicit friends identification over heterogeneous network for social recommendation

The explicitly observed social relations from online social platforms have been widely incorporated into recommender systems to mitigate the data sparsity issue. However, the direct usage of explicit social relations may lead to an inferior performance due to the unreliability (e.g., noises) of observed links. To this end, the discovery of reliable relations among users plays a central role in advancing social recommendation. In this paper, we propose a novel approach to adaptively identify implicit friends toward discovering more credible user relations. Particularly, implicit friends are those who share similar tastes but could be distant from each other on the network topology of social relations. Methodologically, to find the implicit friends for each user, we first model the whole system as a heterogeneous information network, and then capture the similarity of users through the meta-path based embedding representation learning. Finally, based on the intuition that social relations have varying degrees of impact on different users, our approach adaptively incorporates different numbers of similar users as implicit friends for each user to alleviate the adverse impact of unreliable social relations for a more effective recommendation. Experimental analysis on three real-world datasets demonstrates the superiority of our method and explain why implicit friends are helpful in improving social recommendation

Recommender systems based on generative adversarial networks: A problem-driven perspective

Recommender systems (RS) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from a sea of options. Owing to their effectiveness, RS have been widely employed in our daily life. However, despite their empirical successes, these systems still suffer from two limitations: data noise and data sparsity. In recent years, generative adversarial networks (GANs) have garnered increased interest in many fields due to their strong capacity to learn complex real data distributions. Their abilities to enhance RS by tackling the above challenges have also been demonstrated in numerous studies. In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation—implemented by capturing the distribution of real data under the minimax framework—is the primary coping strategy. To gain a comprehensive understanding of these research efforts, we review the corresponding studies and models, organizing them from a problem-driven perspective. More specifically, we propose a taxonomy of these models, along with their detailed descriptions and advantages. Finally, we elaborate on several open issues and current trends in GAN-based RS