Knowledge tracing aims to model learners’ cognitive state dynamically from interaction sequences to support personalized instructional decisions. While existing methods achieve good prediction accuracy, they often overlook the transfer effects between knowledge concepts (KCs) and their propagation, limiting fine-grained, structured modeling of mastery and overall performance. Although some studies incorporate knowledge transfer using predefined KC similarity graphs, they assume static transfer structures for all learners, neglecting the continuous evolution of transfer abilities due to interventions and self-regulation, and focus solely on KC-hierarchy relations. To overcome these limitations, we propose MHAKT. MHAKT operates across multiple hierarchies of knowledge components through three modules: 1) Transfer perception module utilizes a masked attention mechanism to identify the contribution of the Top- \(K\) most relevant historical interactions for the target knowledge component, dynamically updating the learner-specific transfer structure; 2) Knowledge transfer module employs hypergraph neural networks to comprehensively model many-to-many transfer processes among knowledge components; 3) Cognition update module consolidates new knowledge while applying forgetting mechanisms to update the learner's cognitive state. Extensive experiments on benchmark datasets demonstrate that MHAKT significantly outperforms thirteen baseline models. In particular, under data sparsity and generalization settings designed to simulate cold-start knowledge components, MHAKT shows strong robustness and maintains superior predictive accuracy. Ablation studies and exploratory experiments further validate the essential contribution of each module, and visualization analyses further reveal MHAKT's potential for explainable modeling.

Sequential recommendation systems have become essential for personalized services in e-commerce and content platforms. While recent research has extended these systems with multi-modal features, existing approaches face three major challenges. First, they inadequately model fine-grained temporal interval distributions, failing to discriminate between high-frequency short intervals and low-frequency long intervals. Second, uniform fusion in the time domain leads to semantic misalignment across modalities because it ignores their inherent differences in the frequency domain. Third, rigid fusion strategies without self-supervised constraints lead to limited representation quality and semantic drift from pre-trained embeddings. To address these issues, we propose ATHWE, an A daptive T emporal Expert Routing with H ierarchical W avelet E nhancement framework. ATHWE employs exponential saturation time mapping to generate temporally adaptive embeddings. These embeddings guide a sparse mixture of experts to model multi-scale user behavior dynamics. A hierarchical wavelet decomposition with band-specific gating selectively fuses complementary frequency components across modalities. Furthermore, contrastive learning and cluster-preserving objectives preserve semantic information during multi-modal fusion. Extensive experiments on multiple datasets validate the effectiveness of our framework. Our code is available at https://github.com/lulusiyuyu/ATHWE .

The geographic location of social media users is crucial for understanding user behavior, optimizing advertising, and supporting location-based services such as emergency awareness and event monitoring services. However, existing Twitter user geolocation methods primarily focus on explicit social relationships between users while overlooking implicit location correlations, which affects the accuracy of user geolocation. To address this, this paper proposes a Twitter user geolocation method (GeoICMF) based on implicit location correlations and multi-scale feature fusion. GeoICMF introduces a novel location association graph construction method to effectively capture implicit location correlations among users, an innovative multi-scale feature fusion model to dynamically fuse multi-scale features and generate richer user representations, and a pioneering geographic partitioning method to better adapt to user location distributions and enhance geolocation accuracy. Extensive experiments on three real-world datasets demonstrate that GeoICMF outperforms state-of-the-art baseline methods in Twitter user geolocation tasks, validating the effectiveness and superiority of the proposed method.

Modeling high-order feature interactions is crucial for click-through rate (CTR) prediction, yet traditional approaches typically predefine a maximum interaction order and exhaustively enumerate feature combinations up to that order. This paradigm depends heavily on prior domain knowledge to delimit the interaction space and incurs substantial computational overhead. As a result, conventional CTR models face a persistent tension between enriching representations with complex high-order interactions and keeping computation tractable. To address this dual challenge, this study introduces the Kolmogorov-Arnold Represented Sparse Efficient Interaction Network (KarSein). Drawing inspiration from the learnable activation mechanism in the Kolmogorov-Arnold Network (KAN), KarSein leverages this mechanism to adaptively transform low-order basic features into high-order feature interactions, offering a novel approach to feature interaction modeling. KarSein extends the capabilities of KAN by introducing a more efficient architecture that significantly reduces computational costs while accommodating two-dimensional embedding vectors as feature inputs. Furthermore, it overcomes the limitation of KAN’s its inability to spontaneously capture multiplicative relationships among features. Extensive experiments highlight the superiority of KarSein, demonstrating its ability to surpass not only the vanilla implementation of KAN in CTR prediction tasks but also other baseline methods. Remarkably, KarSein achieves exceptional predictive accuracy while maintaining a highly compact parameter size and minimal computational overhead. Moreover, KarSein retains the key advantages of KAN, such as strong interpretability and structural sparsity. As the first systematic adaptation of KAN to CTR prediction, KarSein offers a practical, parameter-efficient, and interpretable alternative for modeling complex feature interactions in large-scale recommendation systems 1 .

Recently, Large Language Model (LLM)-empowered recommender systems have revolutionized personalized recommendation frameworks and attracted extensive attention. Despite the remarkable success, existing LLM-empowered RecSys have been demonstrated to be highly vulnerable to minor perturbations. To mitigate the negative impact of such vulnerabilities, one potential solution is to employ collaborative signals based on item-item co-occurrence to purify the malicious collaborative knowledge from the user’s irrelevant historical interactions. On the other hand, due to the capabilities to expand insufficient internal knowledge of LLMs, Retrieval-Augmented Generation (RAG) techniques provide unprecedented opportunities to enhance the robustness of LLM-empowered recommender systems by introducing external collaborative knowledge. Therefore, in this paper, we propose a novel framework ( RETURN ) by retrieving external collaborative signals to purify the poisoned user profiles and enhance the robustness of LLM-empowered RecSys in a plug-and-play manner. Specifically, retrieval-augmented perturbation positioning is proposed to identify potential perturbations within the users’ historical sequences by retrieving external knowledge from collaborative item graphs. After that, we further retrieve the collaborative knowledge to cleanse the perturbations by using either deletion or replacement strategies and introduce a robust ensemble recommendation strategy to generate final robust predictions. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed RETURN.

Click modeling constitutes a pivotal area of study within information retrieval, as it provides insights into user search behavior and enables the extraction of valuable implicit relevance feedback from large-scale click logs. However, existing click models often rely on content-agnostic IDs to represent queries and documents. Additionally, they employ context-independent assumptions, such as the examination hypothesis, in modeling click probabilities. As a result, contemporary click models often fall short of capturing the influence of the diverse, multi-modal content found on modern Search Engine Result Pages (SERPs) and the intricate contextual interactions among heterogeneous search results. To address this issue, we propose a novel Dynamic Graph Neural Click Model (DGCM). The proposed model incorporates rich content and context information by jointly representing them as nodes in a dynamic graph and further leverages a dynamic graph attention network to predict users’ clicks at different timesteps. To demonstrate the effectiveness of the DGCM model, we conducted extensive experiments on two large-scale datasets with both content and click information: the public Sogou-SRR dataset and a proprietary dataset collected on the WeChat platform. The experimental results indicate that by capitalizing on the content and context information, DGCM outperforms existing click models in the click prediction and relevance estimation tasks.

Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of “information cocoons”, interaction sparsity, cold-start problem and feedback loops inherent in RS make users interact with a limited number of items. Conventional recommendation algorithms typically focus on the positive historical behaviors, while neglecting the essential role of negative feedback in user preference understanding. As a promising but easy-to-ignored area, negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors, emerging as an inescapable procedure in RS. In this survey, we first discuss existing user feedback, the critical role of negative sampling and the optimization objectives in RS and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in RS and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse RS scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.

Recommendations usually focus on immediate accuracy metrics like CTR, ignoring user long-term metrics. User retention, which reflects the percentage of today's users who will return to the system in the next few days, should be paid more attention to. However, most existing methods did not focus on user retention, since their complexity and uncertainty make it extremely hard to discover why a user will or will not return to a system. Recently, a few pioneers have optimized user retention, focusing solely on accuracy without delving into its underlying rationale. This is primarily due to the absence of explicit supervised signals. In this work, we design a behavior-wise contrastive multi-instance learning module (BCMIL), which jointly models clicked and impressed items to capture interpretable user retention. Specifically, we conduct in-depth analyses in real-world scenarios to discover implicit retention-related supervised signals. To model these signals, we design a forward supervised signal extractor (FSSE) that utilizes a heterogeneous graph, enhancing the reliability of user retention. To mitigate randomness and uncertainty, we propose a backward supervised signals stabilizer (BSSS) that utilizes overlooked label-part behaviors within each training window to retrospectively guide the training process. Offline and online evaluations of an industrial system verify the effectiveness of our methods.

In large-scale Point-of-Interest recommendation, the conflict between accuracy and computational efficiency intensifies as POI catalogs grow. Traditional deep models struggle to balance quality with efficiency. To address this challenge, we propose a knowledge-distilled product quantization framework EffiPOI for efficient POI recommendation. EffiPOI jointly optimizes accuracy and efficiency by integrating product quantization with multi-modal knowledge distillation. Specifically, we first construct service-oriented multi-modal POI representations, which comprehensively capture each POI's spatial coverage, temporal activity patterns, and semantic attributes. Based on these representations, we design a teacher-student distillation paradigm. The teacher model adopts a Mixture-of-Experts architecture to generate discriminative and semantically expressive POI representations, which serve as high-quality supervision signals for guiding the student model through knowledge distillation. The student model leverages product quantization to encode POIs into compact and computation-friendly representations, achieving a favorable trade-off between representational compactness and predictive accuracy. To alleviate the performance degradation due to quantization, we develop a hybrid knowledge distillation strategy that transfers both response-aware and feature-aware knowledge from the teacher model to the student model. Experimental results on three real-world datasets show that the proposed method achieves 4.6%-12.1% improvements in accuracy and over 10x speedup in inference efficiency, outperforming existing POI recommendation models. 1

Graphs are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these methods encounter bottlenecks due to the inherent irregularity of graphs. An innovative solution is converting graphs into textual representations, thereby harnessing the powerful capabilities of Large Language Models (LLMs) to process and comprehend graphs. In this paper, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph. The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze. Thus, we propose a novel taxonomy of LLM4graph methods from the view of the transformation. Specifically, existing methods can be divided into two paradigms: Graph2text and Graph2token, which transform graphs into texts or tokens as the input of LLMs, respectively. We point out four challenges during the transformation to systematically present existing methods from a problem-oriented perspective. For practical concerns, we provide a guideline for researchers on selecting appropriate models and LLMs for different graphs and hardware constraints. To empirically evaluate our taxonomy and different technical choices, we conduct experiments with representative methods in Graph2text and Graph2token. We also identify five future research directions for LLM4graph.