Sequential recommenders aim to enhance prediction accuracy by leveraging user interaction sequences, with transformer-based models showing particularly strong performance. Among them, cold-start sequential recommenders are particularly challenging because these models typically require extensive historical data to perform optimally. Some works attempt to address this issue by enhancing the adaptive ability of the sequence recommenders with meta-learning approaches. However, they are unsuitable for enhancing the popular Transformer-based sequence recommenders: MAML-based models cannot adapt the large number of parameters of Transformers, while transition-based and metric-based meta-learning models rely on unique architectures that are incompatible with Transformer-based frameworks. Also, they usually lack mechanisms to recognize and cater to multiple interests within short interaction sequences. To address these limitations, we propose MESA, a meta-modulation plugin module specifically designed to enhance the cold-start recommendation of Transformer-based sequential recommender systems. (1) We design a meta-modulation method to directly modulate the parameters in Transformer-based sequence encoders, thus enabling the model to adapt more effectively to new users in cold-start scenarios. (2) Additionally, MESA integrates the Mixture of Experts (MoE) mechanism, which refines sequence representations by utilizing multiple experts, each focusing on different aspects of user interests. This structure enhances the personalization of the recommendation by effectively handling diverse user interests within the sequences. Experiments demonstrate the effectiveness of MESA in cold-start scenarios. Our codes are available here : https://github.com/Mushroom-cat/MESA .

Graph Contrastive Learning (GCL) has recently garnered significant attention for enhancing recommender systems. Most existing GCL-based methods perturb the raw data graph to generate views, performing contrastive learning across these views to learn generalizable representations. However, most of these methods rely on data- or model-based augmentation techniques that may disrupt interest consistency. In this paper, we propose a novel interest-aware augmentation approach based on diffusion models to address this issue. Specifically, we leverage a conditional diffusion model to generate interest-consistent views by conditioning on node interaction information, ensuring that the generated views align with the interests of the nodes. Based on this augmentation method, we introduce DiffCL, a graph contrastive learning framework for recommendation. Furthermore, we propose an easy-to-hard generation strategy. By progressively adjusting the starting point of the reverse denoising process, this strategy further enhances effective contrastive learning. We evaluate DiffCL on three public real-world datasets, and results indicate that our method outperforms state-of-the-art techniques, demonstrating its effectiveness.

Social recommendation systems leverage both user-user (u-u) social relations and user-item (u-i) collaborative interactions to improve recommendation quality. Despite their effectiveness, existing models often struggle with task-irrelevant information and misalignment between social and collaborative signals and the downstream recommendation task, leading to suboptimal performance. To address these limitations, we propose a novel framework for Effective and Consistent Information Extraction for Social Recommendation (ECSR). Our approach focuses on two key modules: (1) a task-irrelevant information discarding module that filters out noisy signals from both social relations and user-item interactions, and (2) a task-relevant information alignment module that captures both shared and view-specific task-relevant information, ensuring alignment with the recommendation objective. By integrating them into a unified form, our method extracts minimal and sufficient statistics, which significantly enhance the model's ability to predict user preferences. We validate ECSR on three real-world social recommendation datasets, demonstrating that it consistently outperforms state-of-the-art baselines.

Designing effective negative sampling strategies is crucial for training Multi-Modal Recommendation (MMRec) models, as it helps address the issues of sparse user-item interactions and facilitates the learning of high-dimensional modality features. However, most existing methods randomly sample non-interacted items as negative ones, which frequently result in easy negatives. They limit the model's ability to accurately capture user preferences. In this paper, we propose to Generate Difficulty-aware Negative Samples via conditional diffusion for MMRec (denoted as GDNSM). Leveraging the rich semantic and contextual information from multi-modal features, our method generates hard negative samples with varying difficulty levels, tailored to user preferences. They force the model to learn finer-grained distinctions between positive and negative samples, enhancing its ability to inferring user preferences. To avoid unstable training, we design a dynamic difficulty scheduling mechanism that schedules the negative samples from easy to hard for model training, ensuring both stability and effectiveness. Extensive experiments on three real-world datasets demonstrate that the effectiveness of our models.

The integration of social information into recommender systems (RSs) has gained significant popularity for enhancing recommendation performance and user experience. However, this practice introduces substantial privacy risks, particularly concerning the leakage of sensitive social relationships. While prior research has primarily focused on user-level and interaction-level privacy risks, the social relation-level privacy risks remain largely unexplored. To fill this gap, we investigate social privacy risks through membership inference attacks (MIA) and propose a Social relation-level MIA (SMIA) framework. Two key challenges arise: (1) the adversary can only access the recommended item IDs, which provide indirect and limited information about social relationships, and (2) extracting socially relevant preferences from recommendation results is inherently difficult. To tackle the first challenge, we leverage shadow models to transform sparse item IDs into dense features, enabling adversaries to effectively utilize recommendation outputs. For the second challenge, SMIA employs a dual-branch learning approach that disentangles social and behavioral preferences. Therefore, we can extract socially relevant signals from the disentangled preferences.Extensive experiments on real-world datasets demonstrate that both social and general RSs are highly vulnerable to such attacks, highlighting the urgent need for robust privacy protection mechanisms. To defend against these attacks, we introduce a Socially Adversarial Learning (SAL) defense mechanism that selectively obscures sensitive social information in user representations during training, effectively reducing privacy leakage. We further evaluate the effectiveness of our defense and discuss future directions for developing privacy-preserving mechanisms in social RSs.

User cold-start recommendation presents a significant challenge for recommender systems, affecting their overall effectiveness. Meta-learning-based methods have been introduced to address this issue. These methods treat the user cold-start recommendation problem as a few-shot learning task, where each user represents a unique task. The objective is to acquire shared initialization parameters that can be effectively applied across all cold-start users. Subsequently, these shared parameters are fine-tuned into personalized parameters using individual interaction data. Recent studies argue that shared parameters are unsuitable for all users with an implicit grouping distribution of user preference. Therefore, they propose adaptive-initialization-based methods, which first differentiate tasks based on user preferences and then generate task-adaptive initialization parameters using task representations. However, both the meta-learning and adaptive-initialization-based manners ignore discovering the adaptive capability of update strategies in the process of transferring initialization parameters to personalized parameters. Instead, they rely on task-shared optimization strategies, leading the model to fall into an overfitting or underfitting situation. In response to this, we propose a dual-adaptive update strategies-enhanced meta-optimization framework (DAUS) for user cold-start recommendation. First, we integrate dual-adaptive update strategies to enhance the adaptive capability of transferring initialization parameters. This involves incorporating both task-adaptive optimization hyperparameters and objectives. Second, we design a multifaceted task encoder , which can provide diverse task information to differentiate between tasks, including explicit task features (task relevance, training signals) and other implicit task information. Extensive experiments based on three real-world datasets demonstrate that our DAUS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/DAUS .

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques.

Graph-based social recommendation improves the prediction accuracy of recommendation by leveraging high-order neighboring information contained in social relations. However, most of them ignore the problem that social relations can be noisy for recommendation. Several studies attempt to tackle this problem by performing social graph denoising, but they suffer from 1) adaptability issues for other graph-based social recommendation models and 2) insufficiency issues for user social representation learning. To address the limitations, we propose a model-agnostic graph denoising module (denoted as MADM) which works as a plug-and-play module to provide refined social structure for base models. Meanwhile, to propel user social representations to be minimal and sufficient for recommendation, MADM further employs mutual information maximization (MIM) between user social representations and the interaction graph and realizes two ways of MIM: contrastive learning and forward predictive learning. We provide theoretical insights and guarantees from the perspectives of Information Theory and Multi-view Learning to explain its rationality. Extensive experiments on three real-world datasets demonstrate the effectiveness of MADM.

Information networks generally exhibit three characteristics, namely dynamicity, heterogeneity, and node attribute diversity. However, most existing network embedding approaches only consider two of the three when embedding each node into low-dimensional space. Adding to such an existing approach a technique of processing the remaining characteristic can easily cause incompatibility. One solution to process the three characteristics together is to treat the dynamic heterogeneous attributed network (DHAN) as a temporal sequence of heterogeneous attributed network (HAN) snapshots. For example, existing graph convolutional networks (GCNs)-based DHAN embedding approaches embed the HAN snapshots to get static representations offline, and then dynamically capture temporal dependencies between adjacent snapshots online to maintain fresh representations of the DHAN. However, those approaches encounter the convergence problem when stacking multiple convolutional layers to capture more topological information. Some other existing approaches dynamically update the representations of HAN snapshots online, neglecting the efficiency requirement of online scenarios and the temporal dependencies between snapshots. To address the two issues, we propose a new framework called Dynamic Heterogeneous Attributed Network Embedding (DHANE), consisting of a static model MGAT and a dynamic model NICE. MGAT captures more topological information while maintaining GCN convergence by performing metagraph-based attention in each convolutional layer. NICE preserves network freshness while reducing the computational load of the update by only examining network changes and updating their embedding representations. Extensive experiments show that DHANE achieves up to 27× speedup and 9.1-26.4% higher accuracy on several real dynamic heterogeneous attributed networks for online classification.

Sequential recommendation is a critical part of the flourishing online applications by suggesting appealing items on users’ next interactions, where global dependencies among items have proven to be indispensable for enhancing the quality of item representations toward a better understanding of user dynamic preferences. Existing methods rely on pre-defined graphs with shallow Graph Neural Networks to capture such necessary dependencies due to the constraint of the over-smoothing problem. However, this graph representation learning paradigm makes them difficult to satisfy the original expectation because of noisy graph structures and the limited ability of shallow architectures for modeling high-order relations. In this paper, we propose a novel Graph Diffusion Representation-enhanced Attention Network for sequential recommendation, which explores the construction of deeper networks by utilizing graph diffusion on adaptive graph structures for generating expressive item representations. Specifically, we design an adaptive graph generation strategy via leveraging similarity learning between item embeddings, automatically optimizing the input graph topology under the guidance of downstream recommendation tasks. Afterward, we propose a novel graph diffusion paradigm with robustness to over-smoothing, which enriches the learned item representations with sufficient global dependencies for attention-based sequential modeling. Moreover, extensive experiments demonstrate the effectiveness of our approach over state-of-the-art baselines.

User cold-start recommendation is one of the most challenging problems that limit the effectiveness of recommender systems. Meta-learning-based methods are introduced to address this problem by learning initialization parameters for cold-start tasks. Recent studies attempt to enhance the initialization methods. They first represent each task by the cold-start user and interacted items. Then they distinguish tasks based on the task relevance to learn adaptive initialization. However, this manner is based on the assumption that user preferences can be reflected by the interacted items saliently, which is not always true in reality. In addition, we argue that previous approaches suffer from their adaptive framework (e.g., adaptive initialization), which reduces the adaptability in the process of transferring meta-knowledge to personalized RSs. In response to the issues, we propose a task-difficulty-aware meta-learning with adaptive update strategies (TDAS) for user cold-start recommendation. First, we design a task difficulty encoder, which can represent user preference salience, task relevance, and other task characteristics by modeling task difficulty information. Second, we adopt a novel framework with task-adaptive local update strategies by optimizing the initialization parameters with task-adaptive per-step and per-layer hyperparameters. Extensive experiments based on three real-world datasets demonstrate that our TDAS outperforms the state-of-the-art methods. The source code is available at https://github.com/XuHao-bit/TDAS.

Influence maximization through social networks has aroused tremendous interests nowadays. However, people’s various expressions or feelings about a same idea often cause ambiguity via word of mouth. Consequently, the problem of how to maximize the spread of “effective information” still remains largely open. In this paper, we consider a practical setting where ideas can deviate from their original version to invalid forms during message passing, and make the first attempt to seek a union of users that maximizes the spread of effective influence, which is formulated as an Influence Maximization with Information Variation (IMIV) problem. To this end, we model the information as a vector, and quantify the difference of two arbitrary vectors as a distance by a matching function. We further establish a process where such distance increases with the propagation and ensure the recipient whose vector distance is less than a threshold can be effectively influenced. Due to the NP-hardness of IMIV, we greedily select users that can approximately maximize the estimation of effective propagation. Especially, for networks of small scales, we derive a condition under which all the users can be effectively influenced. Our models and theoretical findings are further consolidated through extensive experiments on real-world datasets.

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints that encode heterogeneous behaviors continuously. In this article, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of Long-Short Term Memory (LSTM) and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model.

Influence maximization (IM) aims at maximizing the spread of influence by offering discounts to influential users (called seeding). In many applications, due to user’s privacy concern, overwhelming network scale etc., it is hard to target any user in the network as one wishes. Instead, only a small subset of users is initially accessible. Such access limitation would significantly impair the influence spread, since IM often relies on seeding high degree users, which are particularly rare in such a small subset due to the power-law structure of social networks. In this paper, we attempt to solve the limited IM in real-world scenarios by the adaptive approach with seeding and diffusion uncertainty considered. Specifically, we consider fine-grained discounts and assume users accept the discount probabilistically. The diffusion process is depicted by the independent cascade model. To overcome the access limitation, we prove the set-wise friendship paradox (FP) phenomenon that neighbors have higher degree in expectation, and propose a two-stage seeding model with the FP embedded, where neighbors are seeded. On this basis, for comparison we formulate the non-adaptive case and adaptive case, both proven to be NP-hard. In the non-adaptive case, discounts are allocated to users all at once. We show the monotonicity of influence spread w.r.t. discount allocation and design a two-stage coordinate descent framework to decide the discount allocation. In the adaptive case, users are sequentially seeded based on observations of existing seeding and diffusion results. We prove the adaptive submodularity and submodularity of the influence spread function in two stages. Then, a series of adaptive greedy algorithms are proposed with constant approximation ratio. Extensive experiments on real-world datasets show that our adaptive algorithms achieve larger influence spread than non-adaptive and other adaptive algorithms (up to a maximum of 116 percent).