Cognitive Diagnosis (CD) models are constrained by the data quality of students' response logs. Recent advancements in Large Language Model (LLM) based data augmentation show promise for enhancing CD. However, ensuring the reliability and accuracy of LLM-generated annotations remains a significant challenge. In this paper, we propose Multi-Agent based Concept Augmentation for Cognitive Diagnosis (MACA-CD), a novel approach that enhances CD by generating and fusing reliable concept descriptions and relations based solely on concept names. MACA-CD consists of two main components: (1) a Multi-Agent Debate (MAD) based concept augmentation process that generates diverse and reliable concept descriptions and relations, reducing reliance on behavioral data. For concept descriptions, two agents generate outputs that include definitions, core features, and real-world applications, and continue debating until a judge agent determines that consensus has been reached. Concept relations are then identified using a Breadth-First Search approach to efficiently and progressively uncover relationships based on concept descriptions, with each step carried out by MAD. (2) a concept augmentation-enhanced CD model that refines concept embeddings using a graph self-supervised learning fusion layer and a pairwise comparator-based Description Fusion Layer, leading to more reliable and accurate concept embeddings. Experimental results on three real-world datasets show that MACA-CD consistently outperforms existing methods under various real-world scenarios.

Personalized sequential recommendation aims to predict the appropriate items to users from their behavioral sequences. To alleviate the data sparsity and interest drift issues, conventional approaches typically utilize the additional behaviors from other domains via cross-domain transition. However, existing cross-domain sequential recommendation (CDSR) algorithms follow the align-then-fusion paradigm which conducts the representation-level alignment across multiple domains and mechanically combine them for recommendation, overlooking the fine-grained multi-domain fusion. Inspired by the advancements of diffusion models (DMs) in distribution matching, we propose an align-for-fusion framework for CDSR to Harmonize triple preferences utilizing Dual-oriented DMs (HorizonRec). Specifically, we first investigate the uncertainty injection of DMs and attribute the fundamental factor of the instability in existing DMs recommenders to the stochastic noise and propose a Mixed-conditioned Distribution Retrieval strategy which leverages the retrieved distribution from users' authentic behavioral logic as a bridge across the triple domains, enabling consistent multi-domain preference modeling. To suppress the potential noise and emphasize target-relevant interests during multi-domain user representation fusion, we further propose a Dual-oriented Preference Diffusion method to guide the extraction of preferences aligned with users' authentic interests from each domain under the supervision of the mixed representation. We conduct extensive experiments and analyses on four CDSR datasets from two distinct platforms to verify the effectiveness and robustness of our HorizonRec and its effective mechanism in fine-grained fusion of triple domains. Our code and datasets are available in https://github.com/YongfuZha/HorizonRec.

As Large Language Models (LLMs) increasingly shape online content, how to remove targeted information from well-trained LLMs (also known as LLM unlearning) has become increasingly critical for web governance. A key challenge in LLM unlearning lies in the sample-wise imbalance within the forget set: different samples exhibit widely varying unlearning difficulty, leading to asynchronous forgetting speeds where some knowledge remains insufficiently erased while others become over-forgotten. To address this challenge, we propose BalDRO, a novel and efficient framework for balanced LLM unlearning. BalDRO formulates unlearning as a min–sup process, where the inner process identifies a worst-case data distribution that adaptively emphasizes hard-to-unlearn samples, while the outer process updates model parameters based on the worst-case data distribution. We instantiate this formulation through two efficient variants: BalDRO-G, a discrete GroupDRO-based approximation that focuses on high-loss subsets, and BalDRO-DV, a continuous Donsker–Varadhan dual method that enables smooth, adaptive weighting within standard LLM training pipelines. Extensive experiments on the TOFU and MUSE benchmarks demonstrate the effectiveness of our proposed BalDRO, yielding significant improvements in both forgetting quality and model utility over existing methods. For reproducibility, we have released the code for BalDRO. https://github.com/nxZhai/BalDRO.

Recommender Systems (RS) have been shown to be vulnerable to injective attacks, where attackers inject limited fake user profiles to promote the exposure of target items to real users for unethical gains (e.g., economic or political advantages). Since attackers typically lack knowledge of the victim model deployed in the target RS, existing methods resort to using a fixed surrogate model to mimic the potential victim model. Despite considerable progress, we argue that the assumption thatpoisoned data generated for the surrogate model can be used to attack other victim modelsis wishful. When there are significant structural discrepancies between the surrogate and victim models, the attack transferability inevitably suffers. Intuitively, if we can identify the worst-case victim model and iteratively optimize the poisoning effect specifically against it, then the generated poisoned data would be better transferred to other victim models. However, exactly identifying the worst-case victim model during the attack process is challenging due to the large space of victim models. To this end, in this work, we propose a novel attack method called Sharpness-Aware Poisoning (SharpAP). Specifically, it employs the sharpness-aware minimization principle to seek the approximately worst-case victim model and optimizes the poisoned data specifically for this worst-case model. The poisoning attack withSharpAPis formulated as a min-max-min tri-level optimization problem. By integratingSharpAPinto the iterative process for attacks, our method can generate more robust poisoned data which is less sensitive to the shift of model structure, mitigating the overfitting to the surrogate model. Comprehensive experimental comparisons on three real-world datasets demonstrate thatSharpAPcan significantly enhance the attack transferability.

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence Criterion.Finally, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

Sequential recommendation aims to learn users' dynamic preferences from their historical interactions and predict the next item they are most likely to engage with. In real-world scenarios, time-varying factors (e.g., product promotions, seasonal changes) induce distribution shifts in user interactions. Despite the demonstrated success of existing models, their generalization capability remains limited under such dynamic conditions. Current methods tackle this challenge by leveraging distributionally robust optimization (DRO) to optimize the "worst-case" loss or by employing manually designed data augmentation to enrich the training distribution. Despite their effectiveness, DRO-based approaches are inherently constrained by the sparsity of training data, limiting the range of distributions they can model, while manually designed augmentations risk introducing noise or irrelevant information that could distort user preference learning. Furthermore, these methods often overlook the sensitivity of user interactions to distribution shifts, which is essential for capturing the stable factors in the evolution of user preferences in real-world settings.

Graph-based social recommender systems have demonstrated great potential in alleviating data sparsity by leveraging high-order user influence embedded in social networks.However, most existing methods rely heavily on the observed social graph, which is often noisy and includes spurious or task-irrelevant connections that can mislead user preference learning.Identifying and removing these noisy relations is crucial but challenging due to the lack of ground-truth annotations.In this paper, we approach the social denoising problem from the perspective of graph invariant learning and propose a novel approach, Social Graph Invariant Learning(SGIL).Specifically, SGIL aims to uncover stable user preferences within the input social graph, thereby enhancing the robustness of

Personalized learner modeling uses learners' historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD).This is essential for web-based learning services such as learning resource recommendation and adaptive testing.However, prior studies have shown that CD models may unfairly correlate learners' abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes.While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical.Furthermore, the presence of multiple sensitive attributes complicates fairness improvements.In this paper, we explore how to achieve fair personalized learner modeling without * Min Hou is the corresponding author.

Cold-start recommendation is a long-standing challenge when presenting potential preferred items to new users. Most empirical studies leverage side information to promote cold-start recommendation. In this work, we focus on cross-domain cold-start recommendation, which aims to provide suggestions to those non-overlapping users who have only interacted in the source domain and are viewed as new users in the target domain. Pre-training and then mapping is the common solution for the cross-domain cold-start recommendation. The former learns domain-specific user preference, and the latter transfers preference knowledge from the source to the target domain. Despite the effectiveness, we argue that current mapping-based methods still have the following limitations. First, current mapping functions fail to fully consider the similarity of user behavioral patterns, either common transfer or personalized transfer mappings. Second, sparse supervision signals from the limited overlapping users, lead to insufficient mapping function learning for recommendation. To tackle the above limitations, we propose a novelMACDRmodel for cross-domain cold-start recommendation. Specifically,MACDRconsists of two elaborate modules: a Prototype enhanced Mixture-Of-Experts (PMOE) based mapping function and a Preference Distribution Alignment (PDA) enhanced optimization. PMOE is designed to balance the transfer patterns of common and personalized preferences, following the basis that similar users share similar preference transfer. Furthermore, to alleviate the sparse supervision issue, PDA is designed to explore the utilization of non-overlapping users in an unsupervised manner based on the prototype distribution alignment technique. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method.

As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (such as misclicks or non-preferential behaviors), which will affect precise user preference learning. To overcome the noisy samples problem, a popular solution is based on dropping noisy samples in the model training phase, which follows the observation that noisy samples have higher training losses than clean samples. Despite the effectiveness, we argue that this solution still has limits. (1) High training losses can result from model optimization instability or hard samples, not just noisy samples. (2) Completely dropping of noisy samples will aggravate the data sparsity, which lacks full data exploitation.

With the emergence of social networks, social recommendation has become an essential technique for personalized services.Recently, graph-based social recommendations have shown promising results by capturing the high-order social influence.Most empirical studies of graph-based social recommendations directly take the observed social networks into formulation, and produce user preferences based on social homogeneity.Despite the effectiveness, we argue that social networks in the real-world are inevitably noisy (existing redundant social relations), which may obstruct precise user preference characterization.Nevertheless, identifying and removing redundant social relations is challenging due to a lack of labels.In this paper, we focus on learning the denoised social structure to facilitate recommendation tasks from an information bottleneck perspective.Specifically, we propose a novel Graph Bottlenecked Social Recommendation (GBSR) framework to tackle the social noise issue.GBSR is a model-agnostic social denoising framework, that aims to maximize the mutual information between the denoised social graph and recommendation labels, meanwhile minimizing it between the denoised social graph and the original one.This enables GBSR to learn the minimal yet sufficient social structure, effectively reducing redundant social relations and enhancing social recommendations.Technically, GBSR consists of two elaborate components, preference-guided social graph refinement, and HSIC-based bottleneck learning.Extensive experimental results demonstrate the superiority of the proposed GBSR , including high performances and good generality combined with various backbones.Our code is available at: https://github.com/yimutianyang/KDD24-GBSR.

Social recommendation provides an auxiliary social network structure to enhance recommendation performances. By formulating user-user social network and user-item interaction graph, modern social recommendation architecture is built on learning user and item embeddings into Euclidean space with graph convolution operations. However, the Euclidean space suffers structure distortion when representing the nature power-law distribution of graphs, leading to sub-optimal results for graph based social recommendation. Recently, some studies have explored the alternative of graph embedding learning into hyperbolic space, which can preserve the hierarchy of real-world graphs. However, directly applying current hyperbolic graph embedding models for social recommendation is non-trivial as two challenges: network heterogeneity and social diffusion noise. First, due to the semantic gap existing between social networks and user-item interactions, how to tackle the heterogeneity issue of social recommendation under hyperbolic formulation? Second, explicit modeling of social diffusion easily introduces noise for user preference learning, especially for those active users with amounts of interactions. To tackle the above challenges, in this paper, we propose aHyperbolic Graph Learning based Social Recommendation (HGSR)model. Firstly, we exploit social structure with hyperbolic social embedding pre-training, which could preserve the hierarchical properties of social networks. Secondly, we construct the heterogeneous graph based on user-item interactions and social networks, then treat the pre-trained social embeddings as an additional feature input for user preference learning. Such that, we combine explicit heterogeneous graph learning and implicit feature enhancement for the hyperbolic social recommendation, which can well tackle heterogeneity and social noise issues. We conduct empirical studies on four datasets, and extensive experiments demonstrate the effectiveness of our proposed model compared to state-of-the-art baselines.

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering~(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning~(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then provides self-supervised signals by maximizing the mutual information between contrastive views. Despite the effectiveness, we argue that current GCL-based recommendation models are still limited as current data augmentation techniques, either structure augmentation or feature augmentation. First, structure augmentation randomly dropout nodes or edges, which is easy to destroy the intrinsic nature of the user-item graph. Second, feature augmentation imposes the same scale noise augmentation on each node, which neglects the unique characteristics of nodes on the graph.

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings based on the user-item bipartite graph structure, and have achieved state-of-the-art recommendation performance. In the ubiquitous implicit feedback based CF, users' unobserved behaviors are treated as unlinked edges in the user-item bipartite graph. As users' unobserved behaviors are mixed with dislikes and unknown positive preferences, the fixed graph structure input is missing with potential positive preference links. In this paper, we study how to better learn enhanced graph structure for CF. We argue that node embedding learning and graph structure learning can mutually enhance each other in CF, as updated node embeddings are learned from previous graph structure, and vice versa ~(i.e., newly updated graph structure are optimized based on current node embedding results). Some previous works provided approaches to refine the graph structure. However, most of these graph learning models relied on node features for modeling, which are not available in CF. Besides, nearly all optimization goals tried to compare the learned adaptive graph and the original graph from a local reconstruction perspective, whether the global properties of the adaptive graph structure are modeled in the learning process is still unknown. To this end, in this paper, we propose an enhanced graph learning network EGLN approach for CF via mutual information maximization. The key idea of EGLN is two folds: First, we let the enhanced graph learning module and the node embedding module iteratively learn from each other without any feature input. Second, we design a local-global consistency optimization function to capture the global properties in the enhanced graph learning process. Finally, extensive experimental results on three real-world datasets clearly show the effectiveness of our proposed model.

With the increasing availability of videos, how to edit them and present the most interesting parts to users, i.e., video highlight, has become an urgent need with many broad applications. As users' visual preferences are subjective and vary from person to person, previous generalized video highlight extraction models fail to tailor to users' unique preferences. In this paper, we study the problem of personalized video highlight recommendation with rich visual content. By dividing each video into non-overlapping segments, we formulate the problem as a personalized segment recommendation task with many new segments in the test stage. The key challenges of this problem lie in: the cold-start users with limited video highlight records in the training data and new segments without any user ratings at the test stage. To tackle these challenges, an intuitive idea is to formulate a user-item interaction graph and perform inductive graph neural network based models for better user and item embedding learning. However, the graph embedding models fail to generalize to unseen items as these models rely on the item content feature and item link information for item embedding calculation. To this end, we propose an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec). TransGRec is composed of two parts: a graph neural network followed by an item embedding transfer network. Specifically, the graph neural network part exploits the higher-order proximity between users and segments to alleviate the user cold-start problem. The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase. We design two detailed implementations of the transfer learning optimization function, and we show how the two parts of TransGRec can be efficiently optimized with different transfer learning optimization functions. Please note that, our proposed framework is generally applicable to any inductive graph based recommendation model to address the new node problem without any link structure. Finally, extensive experimental results on a real-world dataset clearly show the effectiveness of our proposed model.

In many recommender systems, users and items are associated with attributes, and users show preferences to items. The attribute information describes users'(items') characteristics and has a wide range of applications, such as user profiling, item annotation, and feature-enhanced recommendation. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. Therefore, item recommendation and attribute inference have become two main tasks in these platforms. Researchers have long converged that user(item) attributes and the preference behavior are highly correlated. Some researchers proposed to leverage one kind of data for the remaining task, and showed to improve performance. Nevertheless, these models either neglected the incompleteness of user~(item) attributes or regarded the correlation of the two tasks with simple models, leading to suboptimal performance of these two tasks.