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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 self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ., Comment: 11 pages, Accepted by WWW'21. Correct some typos in the previous version

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 tri-training framework for improving recommendation. The code is released at https://github.com/Coder-Yu/QRec., Comment: 9 pages, accepted by KDD'21

The proton exchange membrane fuel cell (PEMFC) is one of promising zero emission tools which are applied in the automobile. The PEMFC system is the integration which convert the chemical energy of hydrogen to the electricity and provide the driving force to the vehicle. The hydrogen recirculation loop is commonly used in the vehicle fuel cell system to get a higher fuel consumption efficiency. However, the block issues can happen in the hydrogen recirculation loop and the performance of fuel cell system decreases. The failure mode and mechanisms caused by block issues in hydrogen recirculation loop are shortly reviewed including the water management and nitrogen blanketing. These problems may cause the fuel starvation, carbon corrosion and fuel cell system performance decreasing. Regard to the response option, some counter measures are adapted including the hydrogen purge and hydrogen oscillation.

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user-item interaction data. However, 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 social relations; (2) Social relations are noisy but they are indiscriminately used; (3) Social relations are assumed to be universally applicable to multiple scenarios while they are actually multi-faceted and show heterogeneous strengths in different scenarios. Most existing social recommendation models only consider the homophily in social networks and neglect these drawbacks. In this paper we propose a deep adversarial framework based on graph convolutional networks (GCN) to address these problems. Concretely, for (1) and (2), a GCN-based autoencoder is developed to augment the relation data by encoding high-order and complex connectivity patterns, and meanwhile is optimized subject to the constraint of reconstructing the social profile to guarantee the validity of the identified neighborhood. After obtaining enough purified social relations for each user, a GCN-based attentive social recommendation module is designed to address (3) by capturing the heterogeneous strengths of social relations. Finally, we adopt adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance. Extensive experiments on multiple open datasets demonstrate the superiority of our framework and the ablation study confirms the importance and effectiveness of each component., Accepted by TKDE

In polymer electrolyte fuel cells of the types PEFC, DMFC and HT-PEFC, the gas diffusion layer (GDL) connects the electrodes with the feeding channels of the bipolar plate. The GDL is typically composed of materials based on carbon fibers, e.g., paper, woven or non-woven textiles. Efficient fuel cell operation requires that the electrodes are sufficiently supplied by gaseous fluids from the channels. Also, reaction products must be transported away from the electrodes. The GDL also has to provide electronic contact to the bipolar plates, but its major task is the mass transport of fluids. The gas transport in through-plane direction is simulated in the porous structure of the GDL, represented by stochastic geometries equivalent to the real structure. In order to support multi-scale simulation, effective properties can be calculated from these mesoscale simulation results to provide model parameters for continuum approaches in cell-scale simulations. In this paper, the resulting gas flow is analyzed with statistical methods with the focus on the interface between GDL and electrode. This approach provides the opportunity to detect quantitative relationships between functionality and microstructure and to design virtual GDL materials with improved transport properties. The evaluation of the interface with stochastic methods provides substantiated properties suitable for connecting regions representing fuel cell components of different spatial scales.

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 friends. Most existing social recommenders are based on matrix factorization, a collaborative filtering model that has been proven to be effective. In this paper, we introduce a novel social recommender based on the idea that distance reflects likability. Compared with matrix factorization, the proposed model enables us to obtain a spatial understanding of the latent factor space and how users and items are positioned inside the space by combining the factorization model and distance metric learning. In our method, users and items are initially mapped into a unified low-dimensional space. The positions of users and items are jointly determined by ratings and social relations, which can help to determine appropriate locations for users who have few ratings. Finally, the learned metrics and positions are used to generate understandable and reliable recommendations. Experiments conducted on real-world data sets have shown that compared with methods based on only matrix factorization, our method significantly improves the recommendation accuracy.

Online music recommender systems are becoming increasingly prevalent because of the popularity of digital music and music recommendation generally caters to users by discovering songs that match their preferences. However, these systems have to face a challenge: how to recommend new songs in a situation where prior knowledge is scarce. Some researches take auxiliary information into consideration in new recommendation approaches to deal with this problem. Nevertheless, they rarely pay attention to complex relationships among different feature spaces when they map those information to a latent space. To this end, this paper proposes an approach that uses non-linear transformation to integrate different auxiliary information into the songs latent representations. Unlike other studies which directly map auxiliary information to the feature space, the proposed music recommendation model (NeuTrans) maps different information features to low-dimensional vector representation by non-linear neural networks. Specifically, the NeuTrans separately employs matrix factorization and attribute network embedding to extract auxiliary information (historical interaction, network structure and attributes of songs). The feature space of different information is obtained by nonlinearly mapping the feature space of the songs. Experimental analysis on two real-world datasets shows that our framework outperforms the state-of-the- art approaches on Top-N music recommendation.

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 alleviate the data sparsity problem, in this paper we propose UGAN, which is a general adversarial framework for recommendation tasks and consists of a generative model and a discriminative model. In UGAN, the generative model, acts as an attacker to cheat the discriminative model to capture the pattern of the original data input and generate similar user profiles, while the counterpart, the discriminative model aims to distinguish the forged samples from the real data. By competing with each other, two model are alternatively updated like playing a minimax game until the generative model has learned the original data distribution. The experimental results on two real-world datasets, Movielens and Douban, show that the user profiles forged by UGAN can be easily integrated into a wide range of recommendation methods and significantly improve their performance, which provides a promising way to mitigate the adverse impact of missing data.

Gas transport in non-woven gas diffusion layers of a high-temperature polymer electrolyte fuel cell was calculated with the Lattice Boltzmann method. The underlying micro structure was taken from two sources. A real micro structure was analyzed in the synchrotron under the impact of a compression mask mimicking the channel/rib structure of a flow field. Furthermore a stochastic geometry model based on synchrotron X-ray tomography studies was applied. The effect of compression is included in the stochastic model. Gas transport in these micro structures was simulated and the impact of compression was analyzed. Fiber bundles overlaying the micro structure were identified which affect the homogeneity of the gas flow. There are significant deviations between the impact of compression on effective material properties for this type of gas diffusion layers and the Kozeny-Carman equation.

With the growing popularity of online social platforms, it has been universally recognized that incorporating social relations into recommender systems can usually alleviate the problem of data sparsity. However, social recommender systems based on explicit relations are not as successful as expected due to the noise and the social cold issue of explicit social links. The intuition of utilizing explicit relations is that users share similar preferences if they are friends in the social network. In fact, quite a lot of users who are distant from each other in the social network also have similar tastes. The user item network and the user social network can provide useful information that can complement each other, so that exploring the implicit friends using the heterogeneous network they formed would be more helpful. In this paper, we propose an approach IFSR to discover implicit friends over the heterogeneous network to improve the performance of social recommendation. To find out reliable implicit ties, we first model the system as a heterogeneous network upon which both the preferences and social information are coupled. Over the HIN, similarities between each pair of users can be quantified through network embedding based representation learning. To reduce the computational cost while preserving the information embedded in the original networks and uncover the latent information hiding in the HIN, several meaningful meta-paths over the HIN are designed to guide the process of random walks. Finally, the Top-K implicit friends are incorporated into a social bayesian ranking model to enhance the performance of item ranking. Experimental results on three datasets demonstrate IFSR outperforms the state-of-the-art methods and illustrate why the implicit friends are advantageous for social recommendation.

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 recommendation results and cause great harm to recommendation systems. Existing shilling attack detection models are mainly based on statistical measures to extract features like the rating deviation, which are generally susceptible to attack strategies. Once the attacker changes attack strategy, the detection model which is based on the statistical method may fail. Some researchers have identified that implicit features hidden in user-user interactions and user-item interactions can be utilized to solve the problem. Their research aims to learn potential relationship between users to update features. However, the research ignores the significance of learning features by employing label information. To solve this problem, in this paper, we propose a novel detection model, named BayesDetector, which takes not only the user-user and user-item interactions but also the label information into consideration in the process of learning user implicit features. Furthermore, to take full advantage of user labels, the Bayesian model is added to the feature learning. Experiments on two datasets, Amazon and Movielens, show that BayesDetector significantly outperforms the state-of-the-art methods.

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.

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 social relations, such as the noise, lead to a degradation in recommendation quality. To solve the problem, in this paper, a social recommender, SocialEM, which integrates the neural user embedding and collaborative filtering is proposed. Enlightened by the factorization of the word co-occurrence matrix which is equivalent to the skip-gram model in word2vec, SocialEM will jointly decomposes the user-item rating matrix and the user-user co-occurrence matrix with shared user latent factors. For each pair of users, the co-occurrence matrix encodes the number of being trusted together by other users in the social relation network. Experiments conducted on the real-world datasets have shown that the side effect of social relations can be diminished by tuning parameters for SocialEM. And compared with previous studies, our method significantly improves the quality of recommendations.

© 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 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. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the number of unlabeled normal users is far more than spammers’ in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers exactly. We present two steps: one picks out reliable spammers from unlabeled samples which is imposed on a voting classifier; while the other trains a random forest detector from the normal users and reliable spammers. We conduct experiments on two real-world social datasets and show that our method outperforms other supervised methods.

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 significance, because they usually have decisive impacts on the recommendation results. However, it is still lack of research on how the important users make influence to recommendation methods. This paper presents three types of important users and utilizes three social frequently-used recommendation methods to analyze the influence from multiple perspectives. The experiments demonstrate that all the recommendation methods achieve better performance with important users, and the important neighbor users have the greatest impact on the recommendation methods.

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 becomes more and more crucial. Most existing detection models recognize attackers in statistics-based manners. However, they failed in capturing the fine-grained interactions between users and items, leading to a degradation in detection accuracy. In this paper, inspired by the success of word embedding models, we propose a collaborative shilling detection model, CoDetector, which jointly decomposes the user-item interaction matrix and the user-user co-occurrence matrix with shared user latent factors. Then, the learned user latent factors containing network embedding information are used as features to detect attackers. Experiments conducted on simulated and real-world datasets show that CoDetector has a good performance and generalization capacity and outperforms state-of-the-art methods.

The prosperous online music streaming industry makes personalized music recommendation a topic worthy of extensive study. Traditional music recommendation techniques which are based on conventional collaborative filtering or acoustic content features usually sufffer from data sparsity or time-consuming computation problems, respectively. In fact, online music services not only generate listening history for each user but also accumulate a large amount of heterogeneous data including performers, tags, ownerships and so on. Capturing underlying user preference from the heterogeneous data to enhance music recommendation is transparently promising, because on one hand these data can mitigate the sparsity of listening history while incorporating them into recommendation model is computationally affordable. To this end, in this paper we propose a novel music recommendation approach. It first models the music system as a heterogeneous music graph. Then, to make full use of the heterogeneous data, carefully designed meta-paths are used to dig up the information lying in the graph. Finally, we learn user preferences through a combination of Bayesian Personalized Ranking model and heterogeneous embedding representation learning. Extensive experimental analysis on real-world public dataset validates that the proposed approach outperforms the baselines, especially on cold start users.

© 2017 Elsevier B.V. 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 the rating attack and the relation attack, and term it hybrid attack. Furtherly, we explore the impact of the hybrid attack on model-based social recommender systems in multiple aspects. The experimental results show that, the hybrid attack is more destructive than the rating attack in most cases. In addition, users and items with fewer ratings will be influenced more when attacked. Last but not the least, the findings suggest that spammers do not depend on the feedback links from normal users to become more powerful, the unilateral links can make the hybrid attack effective enough. Since unilateral links are much cheaper, the hybrid attack will be a great threat to model-based social recommender systems.

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 aims to make users in recommender systems be spatially close to their friends and items they like, and be far away from items they dislike by connecting factorization model and distance metric learning. In our method, the positions of users and items are decided by the ratings and social relations jointly, which can help to find appropriate locations for users who have few ratings. Finally, the learnt metric and locations are used to generate understandable and reliable recommendations. The experiments conducted on the real-world dataset have shown that, compared with methods only based on factorization, our method has advantages on both interpretability and accuracy.

© Springer International Publishing AG 2017. 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-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the labeled normal users are far more than spammers in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers. Firstly, a classifier is built from a part of normal and unlabeled samples to pick out reliable spammers from unlabeled samples. Secondly, our well-trained detector, which is based on the given normal users and predicted spammers, can distinguish between normal users and spammers. Experiments conducted on real-world datasets show that the proposed method is competitive with supervised methods.

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 users be spatially close to items they like and be far away from items they dislike, by connecting matrix factorization and distance metric learning. The metric and latent factors are trained simultaneously and then used to generate reliable recommendations. The experiments conducted on the real-world datasets have shown that, compared with methods only based on factorization, our method has advantage in terms of accuracy.

Recommender systems (RSs) 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 an array of options. Owing to their effectiveness, RSs have been widely employed in consumer-oriented e-commerce platforms. 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, owing to their strong capacity to learn complex real data distributions; their abilities to enhance RSs by tackling the challenges these systems exhibit 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 RSs., 24 pages, 12 figures, 8 tables, the journal of Information Sciences

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 friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.