Junliang Yu

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Verified

Junliang verified their affiliation via an institutional email.

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• Doctor of Philosophy
• ARC DECRA at The University of Queensland

Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also driven emerging research on their security aspects, where data poisoning attack for targeted item promotion is a typical example. Existing attack mechanisms primarily focus on...

The implicit feedback (e.g., clicks) in real-world recommender systems is often prone to severe noise caused by unintentional interactions, such as misclicks or curiosity-driven behavior. A common approach to denoising this feedback is manually crafting rules based on observations of training loss patterns. However, this approach is labor-intensive...

The rapid growth of graph data poses significant challenges in storage, transmission, and particularly the training of graph neural networks (GNNs). To address these challenges, graph condensation (GC) has emerged as an innovative solution. GC focuses on synthesizing a compact yet highly representative graph, enabling GNNs trained on it to achieve...

The ID-free recommendation paradigm has been proposed to address the limitation that traditional recommender systems struggle to model cold-start users or items with new IDs. Despite its effectiveness, this study uncovers that ID-free recommender systems are vulnerable to the proposed Text Simulation attack (TextSimu) which aims to promote specific...

Modern recommender systems (RS) have profoundly enhanced user experience across digital platforms, yet they face significant threats from poisoning attacks. These attacks, aimed at manipulating recommendation outputs for unethical gains, exploit vulnerabilities in RS through injecting malicious data or intervening model training. This survey presen...

The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In response, graph condensation (GC) emerges as a promising data-centric solution aiming to substitute the large graph with a small yet informative condensed graph to facilitate...

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving user interests, cloud-based recommender systems are periodically updated with new interaction data. However, on-device models struggle to retrain themselves because of lim...

Recommender systems have become a necessity in this In-ternet era to offer personalization. However, in contrast to the increasing ease of model building and deployment, the lack of user behavioral data still remains a major pain point for modern recommender systems that constantly compromises recommendation performance. Recently, self-supervised l...

Heterogeneous graph neural networks (HGNNs) have exhibited exceptional efficacy in modeling the complex heterogeneity in heterogeneous information networks (HINs). The critical advantage of HGNNs is their ability to handle diverse node and edge types in HINs by extracting and utilizing the abundant semantic information for effective representation...

On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, tensor-train decomposition and i...

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 su...

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The underlying principle of CL-based recommendation models is to ensure the consistency between representations derived from different graph augmentations of the user-item bipartite graph. This self-supervised approach allows for the extractio...

On-device session-based recommendation systems have been achieving increasing attention on account of the low energy/resource consumption and privacy protection while providing promising recommendation performance. To fit the powerful neural session-based recommendation models in resource-constrained mobile devices, tensor-train decomposition and i...

Contrastive learning (CL) has recently been demonstrated critical in improving recommendation performance. The fundamental idea of CL-based recommendation models is to maximize the consistency between representations learned from different graph augmentations of the user-item bipartite graph. In such a self-supervised manner, CL-based recommendatio...

Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even including the immediate environmental...

The neural architecture-based recommenders have demonstrated overwhelming advantages over their traditional counterparts. However, the highly sparse user behavior data often bottlenecks deep neural recommendation models to take full advantage of their capacity for better performance. Recently, self-supervised learning (SSL), which can enable traini...

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with rec-ommender systems' needs for tackling the data sparsity issue. A typical pipeline of CL-based recommendation models is first augmenting the user-ite...

Neural architecture-based recommender systems have achieved tremendous success in recent years. However, when dealing with highly sparse data, they still fall short of expectation. Self-supervised learning (SSL), as an emerging technique to learn with unlabeled data, recently has drawn considerable attention in many fields. There is also a growing...

Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user-item interactions. However, most SSL-based recommendation...

With the increasingly fierce market competition, offering a free trial has become a potent stimuli strategy to promote products and attract users. By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy. However, as the critical point...

Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user–item interactions. However, most SSL-based recommendation...

With the increasingly fierce market competition, the free trial has been widely applied as an effective incentive strategy to attract users and promote products. By providing opportunities to experience goods without charge, a free trial offers adopters more direct contact with the products and thus raises their willingness to buy. However, as the...

Contrastive learning (CL) recently has received considerable attention in the field of recommendation, since it can greatly alleviate the data sparsity issue and improve recommendation performance in a self-supervised manner. A typical way to apply CL to recommendation is conducting edge/node dropout on the user-item bipartite graph to augment the...

In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible...

With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Exist...

Session-based recommendation targets next-item prediction by exploiting user behaviors within a short time period. Compared with other recommendation paradigms, session-based recommendation suffers more from the problem of data sparsity due to the very limited short-term interactions. Self-supervised learning, which can discover ground-truth sample...

To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible but less effective due to simplistic handcrafted rules, while upgraded attacks are more powerful but costly and difficult to deploy because they require more knowledge f...

Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user behaviors but also make the recommendation results explainable by aligning the users/items with various types of entities in the network. Over the past few years, path-bas...

To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible but less effective due to simplistic handcrafted rules, while upgraded attacks are more powerful but costly and difficult to deploy because they require more knowledge f...

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 differ...

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 differ...

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglec...

In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible...

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:...

Social relations are often used to improve recommendation quality and 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...

Recent reports from industry show that social recommender systems consistently fail in practice. The failure is attributed to: (1) a majority of users only have a very limited number of neighbors in social networks and can hardly benefit from relations; (2) social relations are noisy but they are often indiscriminately used; (3) social relations ar...

Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user behaviors but also make the recommendation results explainable by aligning the users/items with various types of entities in the network. Over the past few years, path-bas...

Recent reports from industry show that social recommender systems consistently fail in practice. According to the negative findings, the failure is attributed to: (1) a majority of users only have a very limited number of neighbors in social networks and can hardly benefit from relations; (2) social relations are noisy but they are often indiscrimi...

Recommender systems (RS) play a very important role in various aspects of people's online life. Many companies leverage RS to help users discover new and favored items. Despite their empirical success, these systems still suffer from two main problems: data noise and data sparsity. In recent years, Generative Adversarial Networks (GANs) have receiv...

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of f...

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often unten-able as the online social networks are quite sparse and a majority of users only have a small number of...

Recommendation systems often fail to live up to expectations in real situations because of the lack of user feedback, known as the data sparsity problem. A large number of existing recommendation methods resort to side information to gain a performance improvement. However, these methods are either too complicated to follow or time-consuming. To al...

Recommendation systems often fail to live up to expectations in real situations because of the lack of user feedback, known as the data sparsity problem. A large number of existing recommendation methods resort to side information to gain a performance improvement. However, these methods are either too complicated to follow or time-consuming. To al...

Social connections have been recognized as the supplementary information to deal with the data sparsity problem in traditional recommender systems. Most of the recent studies in social recommendation assume that people share similar preferences with their explicitly connected friends. However, this assumption is fairly sketchy due to the diversity...

In social network, people generally tend to share information with others, thus, those who have frequent access to the social network are more likely to be affected by the interest and opinions of other people. This characteristic is exploited by spammers, who spread spam information in network to disturb normal users for interest motives seriously...

Social recommendation has attracted increasing attention over the years due to the potential value of social relations, which can be harnessed to mitigate the dilemma of data sparsity in traditional recommender systems. However, recent studies show that social recommenders fail in the practical use in industry for the reason that some problems in s...

The recommender system based on collaborative filtering is vulnerable to shilling attacks due to its open nature. With the wide employment of recommender systems, an increasing number of attackers are disordering the system in order to benefit from the manipulated recommendation results. Therefore, how to effectively detect shilling attacks now bec...

Recommendation methods have attracted extensive attention recently because they intent to alleviate the information overload problem. Among them, the social recommendation methods have become one of the popular research fields because they are benefit to solve the cold start problem. In social recommendation systems, some users are of great signifi...

The recommendation systems have been widely employed due to the effectiveness on mitigating the information overload issue. At present, the recommendation systems have made great progress, but they are under the threat of shilling attack because of their open nature. Shilling attack is the way by which the attackers can manipulate the recommendatio...

The explicitly observed social relations from online social platforms have been widely incorporated into conventional 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 discover...

Social networks act as the communication channels for people to share various information online. However, spammers who generate spam information reduce the satisfaction of common users. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervi...

Recommender systems can help to relieve the dilemma called information overload. Collaborative filtering is a primary approach based on collective historical ratings to recommend items to users. One of the most competitive collaborative filtering algorithm is matrix factorization. In this paper, we proposed an alternative method. It aims to make us...

Traditional recommender systems often suffer from the problem of data sparsity because most users rate only a few of the millions of possible items. With the development of social platforms, incorporating abundant social relationships into recommenders can help to overcome this issue because users' preferences can be inferred from those of their fr...

Social relations can help to relieve the dilemmas called cold start and data sparsity in traditional recommender systems. Most of existing social recommendation methods are based on matrix factorization, which has been proven effective. In this paper, we introduce a novel social recommender based on the idea that distance reflects likability. It ai...

With the growing popularity of the online social platform, the social network based approaches to recommendation emerged. However, because of the open nature of rating systems and social networks, the social recommender systems are susceptible to malicious attacks. In this paper, we present a certain novel attack, which inherits characteristics of...