Sequential recommendation models, particularly those based on attention, achieve strong accuracy but incur quadratic complexity, making long user histories prohibitively expensive. Sub-quadratic operators such as Hyena provide efficient alternatives in language modeling, but their potential in recommendation remains underexplored. We argue that Hyena faces challenges in recommendation due to limited representation capacity on sparse, long user sequences. To address these challenges, we propose HyenaRec, a novel sequential recommender that integrates polynomial-based kernel parameterization with gated convolutions. Specifically, we design convolutional kernels using Legendre orthogonal polynomials, which provides a smooth and compact basis for modeling long-term temporal dependencies. A complementary gating mechanism captures fine-grained short-term behavioral bursts, yielding a hybrid architecture that balances global temporal evolution with localized user interests under sparse feedback. This construction enhances expressiveness while scaling linearly with sequence length. Extensive experiments on multiple real-world datasets demonstrate that HyenaRec consistently outperforms Attention-, Recurrent-, and other baselines in ranking accuracy. Moreover, it trains significantly faster (up to 6× speedup), with particularly pronounced advantages on long-sequence scenarios where efficiency is maintained without sacrificing accuracy. These results highlight polynomial-based kernel parameterization as a principled and scalable alternative to attention for sequential recommendation.
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Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation. Existing LLM-based methods typically represent items as token sequences, and apply attention layers on these tokens to generate recommendations. However, by inheriting the standard attention mechanism, these methods focus on modeling token-level relations. This token-centric focus overlooks the item as the fundamental unit of recommendation, preventing existing methods from effectively capturing collaborative relations at the item level. In this work, we revisit the role of tokens in LLM-driven recommendation and categorize their relations into two types: (1) intra-item token relations, which present the content semantics of an item, e.g., name, color, and size; and (2) inter-item token relations, which encode collaborative relations across items. Building on these insights, we propose a novel framework with an item-aware attention mechanism (IAM) to enhance LLMs for recommendation. Specifically, IAM devises two complementary attention layers: (1) an intra-item attention layer, which restricts attention to tokens within the same item, modeling item content semantics; and (2) an inter-item attention layer, which attends exclusively to token relations across items, capturing item collaborative relations. Through this stacked design, IAM explicitly emphasizes items as the fundamental units in recommendation, enabling LLMs to effectively exploit item-level collaborative relations. Extensive experiments on several public datasets demonstrate the effectiveness of IAM in enhancing LLMs for personalized recommendation.
In real-world scenarios, users tend to engage with a small set of popular items, while a large number of long-tail items receive little to no interaction. This long-tail phenomenon substantially impairs recommendation quality. Although prior approaches have attempted to address this issue, the absence of sufficient collaborative signals remains a major obstacle. With the advent of Large Language Models (LLMs), recent studies have explored leveraging LLM-derived semantics to enrich recommendation models. These approaches aim to incorporate textual or contextual knowledge to compensate for limited user-item interactions. A key challenge, however, lies in effectively integrating semantic signals with collaborative representations, which originate from different modalities and learning dynamics. To tackle this, We propose a novel framework, called FCRLLM (the Flipped Classroom with LLM), for long-tail sequential recommendation that aligns collaborative and LLM-based semantic representations. The flipped classroom mechanism dynamically updates the teacher representation to align with the student's attention, enabling more effective integration of semantic and collaborative information. This alignment is implemented via an energy-based formulation inspired by Hopfield networks. To validate its effectiveness, we conduct extensive experiments on three real-world datasets and demonstrate that FCRLLM consistently improves recommendation performance regardless of item popularity or user activity.
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements also grow. This is mainly caused by the standard attention mechanism. Although there exist many methods employing efficient attention and SSM-based models, these approaches struggle to effectively model long sequences and may exhibit unstable performance on short sequences. To address these challenges, we design a sparse attention mechanism, BlossomRec, which models both long-term and short-term user interests through attention computation to achieve stable performance across sequences of varying lengths. Specifically, we categorize user interests in recommendation systems into long-term and short-term interests, and compute them using two distinct sparse attention patterns, with the results combined through a learnable gated output. Theoretically, it significantly reduces the number of interactions participating in attention computation. Extensive experiments on four public datasets demonstrate that BlossomRec, when integrated with state-of-the-art Transformer-based models, achieves comparable or even superior performance while significantly reducing memory usage, providing strong evidence of BlossomRec's efficiency and effectiveness. The code is available at https://github.com/Applied-Machine-Learning-Lab/WWW2026_BlossomRec.
With the rapid proliferation of short-video platforms and content-driven social networks, sequential recommendation models capable of accurately capturing user interests have become increasingly crucial. Among these, Transformer-based sequential recommendation models have gained widespread adoption due to their superior ability. The positional encoding (PE) in Transformer architectures serves to incorporate positional information into sequences. However, relying solely on original absolute positional information may be insufficient for sequential recommendation models. In contrast, the dwell time after interactions (i.e., the time intervals between consecutive user interactions) provides a more accurate reflection of users' emotional responses and evolving interests. Despite its significance, this aspect has often been overlooked in existing works. To fully utilize this information, our work introduces an adaptive PE method, termed TSAPE (Temporal-Series-Aware Positional Encoding). This approach introduces an innovative modeling of the sequence of time intervals between user interactions, rather than the numerical values of the intervals themselves, thereby capturing real-time feedback on user interests and integrating it with conventional PE mechanisms. Furthermore, we employ multiple layers of one-dimensional convolutional networks and attention mechanisms to endow the features with adaptive capabilities across various time interval scenarios. This enables TSAPE to more accurately capture sequential positional information at any given moment. By enhancing the sequential order information of interactions, TSAPE significantly improves the accuracy of next-item recommendations. We seamlessly integrated our method into several Transformer-based sequential recommendation models and conducted comparisons with state-of-the-art sequential recommendation approaches and widely-used PE methods. The results demonstrate that the integration of TSAPE consistently outperforms the original backbone models and other SOTA methods. The SASRec model integrated with TSAPE achieves an average improvement of 15.61% across three evaluation metrics on four benchmark datasets. Our code has been made publicly available at https://github.com/rongbo-qi/TSAPE_Rec.
Multi-behavior sequential recommendation (MBSR) has garnered significant interest for its potential to mitigate the information overload problem. Most existing MBSR methods focus on recommending items w.r.t. one single target behavior (e.g., purchase). In fact, accurately predicting items to be interacted by other behaviors (e.g., examination and favorite) is also important for user satisfaction and especially long-term user retention. Hence, in this paper, we study an emerging problem called multi-task MBSR (MT-MBSR), which requires to recommend items w.r.t. each behavior. The main challenge of MT-MBSR is how to exploit the synergistic value of different behaviors. As a response, we propose a novel model called attention-based multi-view preference learning (AMPL). Firstly, we design an integrated preference learning (IPL) module, which learns cross-behavior preferences. Secondly, we design a sequence-independent preference learning (SIPL) module, aiming to allow the target behavior to selectively capture the contextual information. Thirdly, we introduce a shared-specific gating mechanism to adaptively fuse the outputs of the above two fine-grained learning modules (i.e., IPL and SIPL) while resolving their inherent incompatibilities, collectively leveraging the synergetic effects. Finally, we design a behavior-independent preference learning (BIPL) module to capture complex sequential dependencies among the items for improved final prediction. We conduct extensive experiments on two public datasets and demonstrate that our AMPL significantly outperforms the baseline models in recommendation accuracy w.r.t each behavior. The datasets and source code are available at https://github.com/ddEdison/AMPL.
Lifelong sequential recommendation aims to model users' long-term interests by leveraging their entire interaction history, but the high computational overhead caused by ultra-long sequences poses a core challenge. To address this, existing methods generally adopt the subsequence learning strategies to shorten the input sequence, which can be divided into two categories: (1) Sequence compression methods compress long sequences into multiple subsequence representations through strategies such as uniform segmentation and clustering; (2) Top-k retrieval methods filter the subsequence related to the target item from long sequences via target attention mechanisms or retrieval mechanisms and learn its representation. However, these two types of methods still face challenges in subsequence representation learning: (1) Sequence compression methods struggle to simultaneously balance the high similarity of items within subsequences and smooth temporal continuity (i.e., small temporal intervals between adjacent items), resulting in incorrect learning of subsequence representations; (2) Top-k retrieval methods lose a large amount of effective context information when the length of the retrieved subsequence is much smaller than the original sequence, resulting in incomplete sequence representations. To overcome these challenges, we propose a novel lifelong sequential recommendation method with adaptive subsequence compression and contextual fusion. Specifically, an adaptive subsequence compression module is first designed: it utilizes gradient policy sampling to achieve adaptive segmentation of subsequences, thereby retaining their temporal continuity, and introduces a reward function to enhance the similarity of items within subsequences. Second, a subsequence context fusion module is constructed: it leverages causal cross-attention to fuse recent interactions with their subsequence context and to capture correlations among recent items, thereby learning more complete and accurate sequence representations. We conduct extensive experiments on three public long-sequence recommendation datasets. Experimental results demonstrate that our proposed method consistently outperforms a variety of strong baselines in both predictive accuracy and computational efficiency.
Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies challenging. To address this, we propose SEMixer, a lightweight multiscale model designed for long-term TSF. SEMixer features two key components: a Random Attention Mechanism (RAM) and a Multiscale Progressive Mixing Chain (MPMC). RAM captures diverse time-patch interactions during training and aggregates them via dropout ensemble at inference, enhancing patch-level semantics and enabling MLP-Mixer to better model multi-scale dependencies. MPMC further stacks RAM and MLP-Mixer in a memory-efficient manner, achieving more effective temporal mixing. It addresses semantic gaps across scales and facilitates better multiscale modeling and forecasting performance. We not only validate the effectiveness of SEMixer on 10 public datasets, but also on the 2025 CCF AlOps Challenge based on 21GB real wireless network data, where SEMixer achieves third place. The code is available at https://github.com/Meteor-Stars/SEMixer.
Detecting social bots is critical to ensuring the security of online discourse and maintaining trust in social networks. Early feature-based and text-based methods often fail against bots that mimic human behavior, and graph-based approaches have emerged to better exploit structural signals. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns, and their multi-relation fusion strategies are typically simplistic, ignoring dependencies between relations and user-specific preferences. To overcome these limitations, we propose MPS-Bot, a model that integrates higher-order structure modeling with user-specific cross-relation dependency learning. MPS-Bot introduces a simplex convolutional layer that leverages simplexes derived from network structures to capture group coordination patterns beyond pairwise connections. In addition, a cross-relation dependency attention mechanism adaptively fuses relation-specific representations according to each user's relational preferences, leading to more discriminative and robust multi-relation representations. Extensive experiments on two widely used Twitter bot detection benchmarks, MGTAB and TwiBot-22, show that MPS-Bot generally outperforms state-of-the-art baselines. These findings highlight the effectiveness of higher-dimensional message passing over simplexes as a powerful approach to unmasking bots in social networks.
Multimodal fake information increasingly threatens the Web ecosystem's trustworthiness and security, making improving detection accuracy a critical scientific challenge. The limited information interaction in traditional multimodal fake news detection methods fails to leverage semantic knowledge to model complex cross-modal forgery patterns and global structural anomalies, restricting the model's capability. To address the issues, this paper proposes a multimodal fake news detection method, SVPF-Net, that centers on semantic-driven visual enhancement and knowledge-aided modality-priority fusion. For visual representation optimization, we design a dual-feature extraction module and a dual-fusion enhancement module. A weighted fusion strategy is employed to construct a structured visual representation that integrates the semantics of local forgeries and global anomalies. Meanwhile, a cross-attention mechanism enables bidirectional alignment and interactive coupling between local and global image features, thereby achieving effective complementarity between local forgery cues and global anomaly patterns. For multimodal fusion, high-quality textual semantic features and visual representations are integrated via a modality-priority progressive fusion strategy that relies on cross-attention. The integration enables robust cross-modal semantic interaction and effectively enhances the efficiency of multimodal feature fusion. Comprehensive experiments validate the optimal performance of SVPF-Net and its ability to enhance interpretable semantics, providing valuable support for the practical application of reliable fake news detection.
Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially the dynamic structural modality. Existing dynamic learning methods are often limited to shallow structures across heterogeneous spaces or simple unispaces, making it difficult to capture deep relation-aware geometric features. 2) At the knowledge fusion level, how to learn evolving multimodal fusion features. Existing knowledge fusion methods based on static coattention struggle to capture the varying historical contributions of different modalities to future events. To this end, we propose DyMRL, a Dynamic Multispace Representation Learning approach to efficiently acquire and fuse multimodal temporal knowledge. 1) For the former issue, DyMRL integrates time-specific structural features from Euclidean, hyperbolic, and complex spaces into a relational message-passing framework to learn deep representations, reflecting human intelligences in associative thinking, high-order abstracting, and logical reasoning. Pretrained models endow DyMRL with time-sensitive visual and linguistic intelligences. 2) For the latter concern, DyMRL incorporates advanced dual fusion-evolution attention mechanisms that assign dynamic learning emphases equally to different modalities at different timestamps in a symmetric manner. To evaluate DyMRL's event forecasting performance through leveraging its learned multimodal temporal knowledge in history, we construct four multimodal temporal knowledge graph benchmarks. Extensive experiments demonstrate that DyMRL outperforms state-of-the-art dynamic unimodal and static multimodal baseline methods.
We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG departs from one-shot, text-heavy retrieval by enumerating bounded-length, semantically weighted candidate paths using a hybrid weighted k-shortest, beam, and constrained random-walk strategy, learning a differentiable path scorer together with a contrastive path encoder and lightweight verifier, and injecting a compact soft mixture of selected path latents into a language model via cross-attention. The system runs inside an iterative Neural-Socratic Graph Dialogue loop in which concise diagnostic messages produced by the language model are mapped to targeted graph edits or seed expansions, enabling adaptive retrieval when the model expresses uncertainty. This combination yields a retrieval mechanism that is both token-efficient and topology-aware while preserving interpretable path-level traces for diagnostics and intervention. We validate S-Path-RAG on standard multi-hop KGQA benchmarks and through ablations and diagnostic analyses. The results demonstrate consistent improvements in answer accuracy, evidence coverage, and end-to-end efficiency compared to strong graph- and LLM-based baselines. We further analyze trade-offs between semantic weighting, verifier filtering, and iterative updates, and report practical recommendations for deployment under constrained compute and token budgets.
Dual History Enhancement with Hybrid Hypergraph-Graph Networks for Temporal Knowledge Graph Reasoning
PDF ↗Temporal Knowledge Graph (TKG) reasoning seeks to predict future events by analyzing historical data, where the effective leverage of both local and global historical facts proves crucial. Existing approaches employ graph neural networks (GNNs) and recurrent neural networks (RNNs) for local evolution patterns, complemented by statistical methods to enhance attention to global facts, demonstrating efficient predictive capabilities. However, traditional GNNs, constrained by their low-order neighborhood aggregation design, inherently fail to model potential high-order dependencies among facts. Furthermore, existing global history modeling approaches may introduce irrelevant historical information that interferes with prediction tasks. To address these limitations, we propose a Dual History-aware HyperGraph Network for TKG reasoning, namely DHHGN. Specifically, for local history modeling, we design a hybrid hypergraph-graph joint recurrent convolution module that simultaneously captures low-order neighborhood information and high-order interaction patterns among entities, employing a gating mechanism to adaptively blend their contributions. For global history modeling, we propose a dual history enhancement module that amplifies attention on pivotal historical facts while ensuring holistic integration of all historical contexts. Extensive experiments on four public benchmarks validate that DualHist-HGN consistently outperforms existing state-of-the-art methods across TKG reasoning tasks.
Web-scale platforms and online services rely on log-based anomaly detection to safeguard availability, latency SLOs, and user experience. In real-world web interactions, system logs often exhibit irregular temporal intervals, bursty densities, and heterogeneous semantics, which pose significant challenges for log anomaly detection. Existing methods such as LSTM and Transformer assume a fixed input window, which conflicts with the inherently irregular nature of system logs. Moreover, most prior works build a single-view representation, overlooking the multi-relational nature of logs. To overcome these challenges, we propose DyLogNet, a dynamic multi-relational graph framework for log anomaly detection. Specifically, this framework constructs a density-aware dynamic graph with variable-length windows, and represents logs from three relational perspectives: temporal co-occurrence, semantic similarity, and anomaly tendency. Next, we design a cross-layer attention mechanism that integrates heterogeneous structures to highlight the most relevant relations and enhance event representations. Furthermore, a cross-snapshot memory injection module updates global memory through a recurrent unit and injects it into current graph representations via an affine transformation, enabling temporal continuity. Experiments on three public log datasets demonstrate that DyLogNet outperforms state-of-the-art methods, especially in few-shot scenarios.
The explosive growth of multimodal web data demands communication that transmits meaning rather than raw bits. Existing semantic-communication systems often fail under noise, missing modalities, and distribution shifts because they optimize surface features instead of modality-invariant knowledge. We present Grasp, a knowledge-centric framework for cross-modal communication. Grasp segments streams into semantic blocks and builds a graph over them; a lightweight Graph Neural Networks (GNN) produces schedulable, importance-weighted representations. At its core is knowledge purification : we minimize a conditional mutual information upper bound to perform a three-way disentanglement—strongly related, weakly related, and task-irrelevant components—so that only essential semantics are transmitted while non-essential factors are suppressed. To maintain synchrony, we introduce one-to-two temporal contrastive learning to achieve triple alignment of video, audio, and text despite sampling asynchrony. For efficient transmission, Grasp uses a cross-modal shared vector-quantization codebook—a discrete knowledge codebook —updated by multimodal attention. At the receiver, a soft-recovery mechanism leverages this shared knowledge to robustly reconstruct semantics under low signal-to-noise ratio (SNR) or missing modalities, yielding graceful degradation. Across web tasks—including cross-modal retrieval and missing-modality inference—Grasp improves knowledge consistency, semantic fidelity, and downstream performance over strong baselines while maintaining low latency. These results show that communication structured around purified knowledge is key to building robust, semantic-aware systems for the modern web.
Text classification is a crucial and fundamental task in web content mining. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification, some limitations remain. First, many text datasets are imbalanced, and the learning mechanism of supervised contrastive learning is sensitive to data imbalance, which may harm the model's performance. Moreover, these models leverage separate classification branches with cross entropy and supervised contrastive learning branches without explicit mutual guidance. To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. First, we obtain the prototype vector of each class in the balanced classification branch to act as a representation of each class. Then, by further explicitly leveraging the prototype vectors, we construct a proper and sufficient target sample set with the same size for each class to perform the supervised contrastive learning procedure. The empirical results show the effectiveness of our model, which even outperforms popular large language models across several datasets. Our code is available https://github.com/KEAML-JLU/SharpReCL
Multi-modal knowledge graphs (MMKGs) enrich traditional knowledge graphs by incorporating heterogeneous modalities such as textual descriptions and visual content, offering complementary semantic cues for knowledge reasoning. However, existing approaches often overlook the structural dependencies within each modality, apply static or coarse-grained fusion strategies, and insufficiently model relational semantics. We propose a Multi-Granularity Multi-Modal Knowledge Graph Representation Learning Method via Subgraph-aware Adaptive Fusion and Hierarchical Relation Modeling (SAFER ), which implement multi-modal knowledge representation through adaptive fusion of multi-granularity information such as multi-modal semantics, knowledge structures and relations. SAFER explicitly constructs modality-specific subgraphs and employs structure-aware graph attention networks to effectively capture intra-modal structural dependencies. We propose an adaptive multi-modal fusion mechanism, which aggregates modality-specific embeddings at the semantic level by dynamically assigning entity-specific modality weights. We design a two-stage multi-granularity knowledge relation modeling strategy, which utilizes a structure-aware multi-modal adaptive pre-fusion to preserve topological information and a relation-aware graph attention network (RGAT) post-fusion to encode relational semantics. Extensive experiments on several benchmark datasets demonstrate that the proposed SAFER significantly outperforms competitive baselines on link prediction and relation reasoning tasks.
Website Fingerprinting (WF) is an emerging traffic analysis technique that enables a passive adversary to infer which websites a user visits. However, most existing studies, whether in single-tab or multi-tab settings, rely on the unrealistic assumption that users only access website homepages, diverging significantly from real-world browsing behavior. Even recent works extending WF to subpages primarily focus on website-level identification, without distinguishing which specific subpages are visited, thereby limiting the attack's granularity and scope. In this paper, we propose HiFi-WF (Hierarchical Fine-grained Website Fingerprinting), a novel framework that breaks the homepage-only assumption and extends WF to multi-tab recognition and fine-grained subpage identification. We formulate the task as a hierarchical multi-label classification problem, jointly modeling the distinctions and correlations between homepages and subpages. To this end, HiFi-WF integrates a unified CNN-based extractor and layered encoder with a Feature Interaction Module based on multi-head cross-attention to capture inter-level dependencies. An Enhanced SubHead enforces hierarchical constraints to suppress invalid subpage predictions, while a cascaded channel–spatial attention mechanism refines discriminative features for precise hierarchical identification. Experimental results demonstrate that HiFi-WF achieves state-of-the-art performance at both hierarchical levels, attaining F1-scores of 92.1% (homepage) and 81.9% (subpage), thereby validating its effectiveness in advancing WF attacks toward realistic, fine-grained, and multi-tab browsing scenarios. Related codes and datasets can be found in https://github.com/wusongyang02-blip/HiFi-WF.
While Tor's strong anonymity shields users' privacy, it also enables malicious activities, motivating attacks that bypass its protections. Website Fingerprinting (WF) has emerged as a primary threat in this domain. However, existing WF methods struggle with realistic multi-tab browsing scenarios, often relying on prior knowledge of the number of open tabs and lacking robustness against network noise and defenses. To address these challenges, we propose Forge, a robust WF attack framework inspired by the classic cocktail party problem. Specifically, Forge reframes multi-tab WF as a task of Blind Source Separation(BSS), decomposing mixed traffic into individual signals without requiring a predefined number of concurrent tabs. A robust website identifier then classifies separated components using a dual-domain attention mechanism across time and frequency, allowing Forge to effectively resist WF defenses and network noise. We evaluate our model on a comprehensive collection of datasets covering open-world, defense-enabled, and dynamic scenarios. The results demonstrate that Forge can improve Mean Average Precision by 78.6% over the state-of-the-art average in the challenging multi-tab open-world scenario.
LLM-powered agents are evolving from passive recommenders into autonomous executors, leveraging tools via the Model Context Protocol (MCP) for web automation. However, this paradigm introduces a new vulnerability: tool poisoning attacks that manipulate the MCP context can corrupt an agent's reasoning. Existing methods focus on anomaly detection and lack autonomous correction mechanisms, hindering their real-world deployment. In this paper, we introduce MCPFixGen, to the best of our knowledge, the first framework to provide automated anomaly detection and correction. We tackle three fundamental challenges: (1) when to initiate anomaly inspection with minimal overhead, (2) what features effectively characterize abnormality, and (3) how to automatically execute remedial actions. MCPFixGen achieves this through a novel multi-checkpoint rollback mechanism for anomaly management. MCPFixGen proposes a multi-checkpoint rollback anomaly management mechanism. Strategically places checkpoints at high-risk operations based on the tools-calling risk distribution. Then it identifies unanticipated tool behaviors by leveraging the correlation between attention activation values and tool calls. Finally, it introduces an attention-mask-based backtracking and correction mechanism. By masking the root cause of the anomaly, the LLM is prompted to re-infer, facilitating efficient and low-cost anomaly resolution. Our evaluation on real-world datasets demonstrates that MCPFixGen reduces interruptions caused by anomalies to below 13%, decreases the success rate of tool poisoning attacks to less than 0.3%, and significantly improves the robustness, computational efficiency, and task completion rate of web intelligence applications compared to existing approaches.