Cross-domain recommendation (CDR) offers an effective strategy for improving recommendation quality in a target domain by leveraging auxiliary signals from source domains. Nonetheless, emerging evidence shows that CDR can inadvertently heighten group-level unfairness. In this work, we conduct a comprehensive theoretical and empirical analysis to uncover why these fairness issues arise. Specifically, we identify two key challenges: (i) Cross-Domain Disparity Transfer, wherein existing group-level disparities in the source domain are systematically propagated to the target domain; and (ii) Unfairness from Cross-Domain Information Gain, where the benefits derived from cross-domain knowledge are unevenly allocated among distinct groups.

Recommender systems have been popularly employed to address information overload problems for users, for which unfairness issues are essential to be mitigated. Current user-side fairness studies in recommendations aim to ensure the independence of users’ sensitive attributes in terms of outcome or process . Specifically, the former emphasizes the outcome metric equity across user groups based on sensitive attributes, for which regularization has been proposed as a typical approach to reducing the recommendation imbalance among them. The latter process fairness focuses on independence between sensitive attributes and recommendations in the process, for which adversarial learning has been popularly adopted to remove sensitive information from user representation for individual users. However, little work has investigated the effects of these methods on balancing both group-level outcome fairness and individual-level process fairness in a particular scenario. Moreover, existing experiments have primarily been performed on one type of user attributes, e.g., behavioral attributes or demographic attributes, but neglected other possible sensitive attributes such as psychological attributes. In this article, we have investigated both outcome and process fairness performance of regularization and adversarial learning methods over multiple types of sensitive attributes, including behavioral (e.g., activity level), demographic (e.g., gender, age), and psychological attributes (e.g., big-five personality, curiosity). The experiments on four datasets show that unfairness can exist in different forms for different types of sensitive attributes, and relative to the regularization-based method, adversarial learning owns a higher potential to achieve a balance between outcome and process user-side fairness in recommendations. Our source code is available at https://github.com/WeixinChen98/OtPrFairness-MultiAttr .

Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain.However, through empirical studies, we uncover a critical bias inherent in these approaches: while overlapping users experience significant enhancements in recommendation quality, non-overlapping users benefit minimally and even face performance degradation.This unfairness may erode user trust, and, consequently, negatively impact business engagement and revenue.To address this issue, we propose a novel solution that generates virtual source-domain users for non-overlapping target-domain users.Our method utilizes a dual attention mechanism to discern similarities between overlapping and non-overlapping users, thereby synthesizing realistic virtual user embeddings.We further introduce a limiter component that ensures the generated virtual users align with real-data distributions while preserving each user's unique characteristics.Notably, our method is model-agnostic and can be seamlessly integrated into any CDR model.Comprehensive experiments conducted on three public datasets with five CDR baselines demonstrate that our method effectively mitigates the CDR nonoverlapping user bias, without loss of overall accuracy.Our code is publicly available at https://github.com/WeixinChen98/VUG.

Community detection plays a pivotal role in uncovering closely connected subgraphs, aiding various real-world applications such as recommendation systems and anomaly detection. With the surge of rich information available for entities in real-world networks, the community detection problem in attributed networks has attracted widespread attention. While previous research has effectively leveraged network topology and attribute information for attributed community detection, these methods overlook two critical issues: (i) the semantic similarity between node attributes within the community, and (ii) the inherent mesoscopic structure, which differs from the pairwise connections of the micro-structure. To address these limitations, we propose HACD, a novel attributed community detection model based on heterogeneous graph attention networks. HACD treats node attributes as another type of node, constructs attributed networks into heterogeneous graph structures and employs attribute-level attention mechanisms to capture semantic similarity. Furthermore, HACD introduces a community membership function to explore mesoscopic community structures, enhancing the robustness of detected communities. Extensive experiments demonstrate the effectiveness and efficiency of HACD, outperforming state-of-the-art methods in attributed community detection tasks. Our code is publicly available at https://github.com/Anniran1/HACD1-wsdm https://github.com/Anniran1/HACD1-wsdm.

Recently, multimodal recommendations (MMRs) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-based representations. Although MMR exhibits notable improvement in recommendation accuracy, we empirically validate that an increase in the quantity or variety of modalities leads to a higher degree of users’ sensitive information leakage due to entangled causal relationships, risking fair representation learning. On the other hand, existing fair representation learning approaches are mostly based on the assumption that sensitive information is solely leaked from users’ interaction data and do not explicitly model the causal relationships introduced by multimodal data, which limits their applicability in multimodal scenarios. To address this limitation, we propose a novel fair multimodal recommendation approach (dubbed FMMRec) through causality-inspired fairness-oriented modal disentanglement and relation-aware fairness learning. Particularly, we disentangle biased and filtered modal embeddings inspired by causal inference techniques, enabling the mining of modality-based unfair and fair user–user relations, thereby enhancing the fairness and informativeness of user representations. By addressing the causal effects of sensitive attributes on user preferences, our approach aims to achieve counterfactual fairness in MMRs. Experiments on two public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://github.com/WeixinChen98/FMMRec .

Negative sampling plays a crucial role in implicit-feedback-based collaborative filtering, where it leverages massive unlabeled data to generate negative signals for guiding supervised learning. The current state-of-the-art approaches focus on utilizing hard negative samples that contain more information to establish a better decision boundary. To strike a balance between efficiency and effectiveness, most existing methods adopt a two-pass approach: In the first pass, a fixed number of unobserved items are sampled using a simple static distribution, while, in the second pass, a more sophisticated negative sampling strategy is employed to select the final negative items. However, selecting negative samples solely from the original items in a dataset is inherently restricted due to the limited available choices and thus may not be able to effectively contrast positive samples. In this article, we empirically validate this observation through meticulously designed experiments and identify three major limitations of existing solutions: ambiguous trap, information discrimination, and false-negative samples. Our response to such limitations is to introduce “denoised” and “augmented” negative samples that may not exist in the original dataset. This direction renders a few substantial technical challenges. First, constructing augmented negative samples may introduce excessive noise that eventually distorts the decision boundary. Second, the scarcity of supervision signals hampers the denoising process. To this end, we introduce, to the best of our knowledge, a novel generic denoising and augmented negative sampling paradigm and provide a concrete instantiation. First, we disentangle the hard and easy factors of negative items. Then, we regulate the augmentation of easy factors by carefully considering the direction and magnitude. Next, we propose a reverse attention mechanism to learn a user’s negative preference, which allows us to perform a dimension-level denoising procedure on hard factors. Finally, we design an advanced negative sampling strategy to identify the final negative samples, taking into account both the score function used in existing methods and, to the best of our knowledge, a novel metric called synthesization gain. Through extensive experiments on real-world datasets, we demonstrate that our method substantially outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole .

Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current research focuses on addressing the challenge of unlabeled data by extracting a subset that closely approximates negative samples. Regrettably, the remaining data are overlooked, failing to fully integrate this valuable information into the construction of user preferences. To address this gap, we introduce a novel positive-neutral-negative (PNN) learning paradigm. PNN introduces a neutral class, encompassing intricate items that are challenging to categorize directly as positive or negative samples. By training a model based on this triple-wise partial ranking, PNN offers a promising solution to learning complex user preferences. Through theoretical analysis, we connect PNN to one-way partial AUC (OPAUC) to validate its efficacy. Implementing the PNN paradigm is, however, technically challenging because: (1) it is difficult to classify unlabeled data into neutral or negative in the absence of supervised signals; (2) there does not exist any loss function that can handle set-level triple-wise ranking relationships. To address these challenges, we propose a semi-supervised learning method coupled with a user-aware attention model for knowledge acquisition and classification refinement. Additionally, a novel loss function with a two-step centroid ranking approach enables handling set-level rankings. Extensive experiments on four real-world datasets demonstrate that, when combined with PNN, a wide range of representative CF models can consistently and significantly boost their performance. Even with a simple matrix factorization, PNN can achieve comparable performance to sophisticated graph neutral networks. Our code is publicly available at https://github.com/Asa9aoTK/PNN-RecBole.

Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary. To balance efficiency and effectiveness, the vast majority of existing methods follow the two-pass approach, in which the first pass samples a fixed number of unobserved items by a simple static distribution and then the second pass selects the final negative items using a more sophisticated negative sampling strategy. However, selecting negative samples from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to contrast positive samples well. In this paper, we confirm this observation via carefully designed experiments and introduce two major limitations of existing solutions: ambiguous trap and information discrimination.

Negative sampling is essential for implicit collaborative filtering to generate negative samples from massive unlabeled data. Unlike existing strategies that consider items as a whole when selecting negative items, we argue that normally user interactions are mainly driven by some relevant, but not all, factors of items, leading to a new direction of negative sampling. In this paper, we introduce a novel disentangled negative sampling (DENS) method. We first disentangle the relevant and irrelevant factors of positive and negative items using a hierarchical gating module. Next, we design a factor-aware sampling strategy to identify the best negative samples by contrasting the relevant factors while keeping irrelevant factors similar. To ensure the credibility of the disentanglement, we propose to adopt contrastive learning and introduce four pairwise contrastive tasks, which enable to learn better disentangled representations of the relevant and irrelevant factors and remove the dependency on ground truth. Extensive experiments on five real-world datasets demonstrate the superiority of DENS against several state-of-the-art competitors, achieving over 7% improvement over the strongest baseline in terms of [email protected] and [email protected] Our code is publically available at https://github.com/Riwei-HEU/DENS .