In e-commerce, recommender systems help users find suitable products by leveraging diverse behaviors, e.g., view, cart and buy. In recent years, multi-behavior recommender systems have made strides by integrating auxiliary behaviors with purchase histories to deliver high-quality recommendations. However, most existing methods often fail to identify spurious correlation intents within auxiliary behaviors that conflict with users' target intents. Indiscriminately incorporating such correlations into the prediction of target intents may lead to performance degradation. Toward this end, we propose a Multi-Behavior Intent Disentanglement (MBID) framework based on Information Bottleneck (IB) principle, which focuses on disentangling spurious correlation intents in multi-behavior recommendations. In particular, we design a projection-based intent extraction method to decompose the genuine and spurious correlation intents in auxiliary behaviors. Building on this, we conceive an IB-based multi-intent learning task to disentangle the spurious correlation intents and transfer the genuine correlation intents from auxiliary behaviors into the target behavior, yielding high-quality target intent representations. Experiments on three real-world datasets show MBID significantly outperforms the state-of-the-art baselines by effectively disentangling the spurious correlation intents.

Graph Convolutional Networks (GCNs) have become increasingly popular in recommendation systems. However, recent studies have shown that GCN-based models will cause sensitive information to disseminate widely in the graph structure, amplifying data bias and raising fairness concerns. While various fairness methods have been proposed, most of them neglect the impact of biased data on representation learning, which results in limited fairness improvement. Moreover, some studies have focused on constructing fair and balanced data distributions through data augmentation, but these methods significantly reduce utility due to disruption of user preferences. In this paper, we aim to design a fair recommendation method from the perspective of data augmentation to improve fairness while preserving recommendation utility. To achieve fairness-aware data augmentation with minimal disruption to user preferences, we propose two prior hypotheses. The first hypothesis identifies sensitive interactions by comparing outcomes of performance-oriented and fairness-aware recommendations, while the second one focuses on detecting sensitive features by analyzing feature similarities between biased and debiased representations. Then, we propose a dual data augmentation framework for fair recommendation, which includes two data augmentation strategies to generate fair augmented graphs and feature representations. Furthermore, we introduce a debiasing learning method that minimizes the dependence between the learned representations and sensitive information to eliminate bias. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed framework.

Graph-structured data is ubiquitous in today's connected world, driving extensive research in graph analysis. Graph Neural Networks (GNNs) have shown great success in this field, leading to growing interest in developing fair GNNs for critical applications. However, most existing fair GNNs focus on statistical fairness notions, which may be insufficient when dealing with statistical anomalies. Hence, motivated by the causal theory, there has been growing attention to mitigating root causes of unfairness utilizing graph counterfactuals. Unfortunately, existing methods for generating graph counterfactuals invariably require the sensitive attribute. Nevertheless, in many real-world applications, it is usually infeasible to obtain sensitive attributes due to privacy or legal issues, which challenge existing methods. In this paper, we propose a framework named Fairwos (improving Fairness withQut sensitive attributes). In particular, we first propose a mechanism to generate pseudo-sensitive attributes to remedy the problem of missing sensitive attributes, and then design a strategy for finding graph counterfactuals from the real dataset. To train fair GNNs, we propose a method to ensure that the embeddings from the original data are consistent with those from the graph counterfactuals, and dynamically adjust the weight of each pseudo-sensitive attribute to balance its contribution to fairness and utility. Furthermore, we theoretically demonstrate that minimizing the relation between these pseudo-sensitive attributes and the prediction can enable the fairness of GNNs. Experimental results on six real-world datasets show that our approach outperforms state-of-the-art methods in balancing utility and fairness.

As Facial Expression Recognition (FER) systems become increasingly integrated into daily life, ensuring fairness in decision-making is crucial, rather than focusing solely on improving recognition performance. FER systems need to find a trade-off between performance and fairness in order to avoid bias to subgroups while ensuring utility. To address this issue, various methods have been proposed at the data and algorithmic levels. However, these studies mainly focus on the unfairness of demographic attributes, and the bias of expression class remains largely unexplored. In this study, we attempt a novel strategy for applying class-incremental learning (class-IL) to mitigate expression class bias. Furthermore, we propose a fair model based on the characteristic of class-IL, called Incremental Expression Balance Network (IEBN). IEBN mitigates this class bias by adjusting the data distribution of different expressions and extracting critical features with an attention mechanism. Finally, experiments on the RAF-DB and AffectNet datasets show that IEBN outperforms other methods in terms of fairness and trade-offs between fairness and performance.

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. Firstly, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Secondly, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users' interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. The source code is available at: https://github.com/FairCoRe2024/FairCoRe.

In many real-world applications, sequential rule mining (SRM) can offer prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that can reveal the temporal relationship between objects. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on the problem of targeted mining. Targeted sequential rule mining aims to obtain those interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. It can further improve the efficiency of users in analyzing rules and reduce the consumption of computing resources. In this paper, we first present the relevant definitions of target sequential rules and formulate the problem of targeted sequential rule mining. Then, we propose an efficient algorithm called TaSRM. Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the baseline algorithm.

Federated Recommender Systems (FedRecs) have evolved as a privacy-preserving paradigm that facilitates distributed training of personalized recommenders without sharing user data. However, FedRecs are known to be susceptible to poisoning attacks by malicious users, who aim at promoting or demoting the exposure of target items through sending malicious updates to the central server. Meanwhile, the distribution of recommendation performance among users, called as performance fairness, could be exacerbated, which is one of the major concerns of trustworthy FedRecs. This paper proposes a novel attack method, Generative Adversarial Network (GAN)-Based Collusive Poisoning Attack (GCPA). To implement GCPA, we create a GAN-based fake user synthesis strategy that mimics behaviors and preferences of real users to generate fake users. Furthermore, we design a collusion-based fairness attack strategy that changes the exposure of items to undermine fairness. To maximize the impact on the distribution of recommendation performance, we develop an adaptive clustering algorithm to identify a subset of items that significantly contribute to the uneven distribution of recommendation performance through collusion. Extensive experiments on two datasets show that GCPA effectively increase the exposure of target items while undermining the performance fairness of FedRecs. In addition, GCPA also has strong resistance to four defense methods. Meanwhile, we provide a heuristic defense method based on gradient direction and similarity against collusive poisoning attack on FedRecs.

Semi-supervised multi-label learning (SSMLL) involves learning a multi-label classifier from a small set of labeled data and a large set of unlabeled data. Label enhancement (LE), accounting for the relative importance of labels, has been effective in improving the performance of supervised multi-label learning models. Nevertheless, generating a robust SSMLL model with LE based on incomplete label information remains challenging. In this paper, we pioneer the idea of applying LE to SSMLL. First, we design a kNN aggregation-based method, aiming to assign pseudo-labels to unlabeled data and perform the LE process by aggregating label information from neighboring instances. Leveraging the topological structure of the feature space is an effective LE approach for training. However, LE, decoupled from the training process, lacks the dynamic feedback of the training model. To improve this, we incorporate a label propagation mechanism that iteratively optimizes the LE process with the guidance of the available label information. Moreover, we consider local label correlations according to local linear embedding to further enhance the generalization ability of the learning model. Extensive experiments demonstrate that the proposed approach can effectively recover latent label information, resulting in significant performance improvement in SSMLL.

Multi-domain fake news detection aims to identify whether various news from different domains is real or fake and has become urgent and important. However, existing methods are dedicated to improving the overall performance of fake news detection, ignoring the fact that unbalanced data leads to disparate treatment for different domains, i.e., the domain bias problem. To solve this problem, we propose the Dual-Teacher De-biasing Distillation framework (DTDBD) to mitigate bias across different domains. Following the knowledge distillation methods, DTDBD adopts a teacher-student structure, where pre-trained large teachers instruct a student model. In particular, the DTDBD consists of an unbiased teacher and a clean teacher that jointly guide the student model in mitigating domain bias and maintaining performance. For the unbiased teacher, we introduce an adversarial de-biasing distillation loss to instruct the student model in learning unbiased domain knowledge. For the clean teacher, we design domain knowledge distillation loss, which effectively incentivizes the student model to focus on representing domain features while maintaining performance. Moreover, we present a momentum-based dynamic adjustment algorithm to trade off the effects of two teachers. Extensive experiments on Chinese and English datasets show that the proposed method substantially outperforms the state-of-the-art baseline methods in terms of bias metrics while guaranteeing competitive performance11Our codes are available at https://github.com/ningljy/DTDBD.

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed “personalized Federated learning with Traffic Pattern Sharing” (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are retained for local training, thereby enabling personalized learning based on regional characteristics. Intensive experiments on four real-world traffic datasets firmly demonstrate the superiority of our proposed FedTPS over other compared typical FL methods in terms of various estimation errors.

Conversational Recommendation Systems (CRSs) enable recommender systems to explicitly acquire user preferences during multi-turn interactions, providing more accurate and personalized recommendations. However, the data imbalance in CRSs, due to inconsistent interaction history among users, may lead to disparate treatment for disadvantaged user groups. In this paper, we investigate the discriminate problems in CRS from the user’s perspective, called as user-oriented fairness. To reveal the unfairness problems of different user groups in CRS, we conduct extensive empirical analyses. To mitigate user unfairness, we propose a user-oriented fairness framework, named FairCRS, which is a model-agnostic framework. In particular, we develop a user-embedding reconstruction mechanism that enriches user embeddings by incorporating more interaction information, and design a user-oriented fairness strategy that optimizes the recommendation quality differences among user groups while alleviating unfairness. Extensive experimental results on English and Chinese datasets show that FairCRS outperforms state-of-the-art CRSs in terms of overall recommendation performance and user fairness.

Correlated high-utility pattern mining (CoUPM) considers the correlation between items in a pattern and offers a more reliable analysis for users. In real-world applications, the discovered patterns from CoUPM can present more interpretable information, but not all of them are useful. Generally, users pay attention to the number of items a pattern contains, which allows them to make reasonable decisions. In this paper, we solve the problem of mining those correlated high-utility patterns whose length is specified. A utility-list-based algorithm called Flexible Correlated Utility-based Pattern (FCoUP) is proposed. Furthermore, we propose some pruning strategies with the designed upper bounds for the two evaluation metrics: correlation and utility, reducing unwanted patterns generated and nodes visited during the mining process. Experiments show that FCoUP variants can produce more intelligent and flexible correlated high-utility patterns on a variety of datasets.