OpenRaiser/Intern-Atlas huggingface dataset:
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Authors: Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju
Venue: WWW
Year: 2025

Collaborative Filtering (CF) methods dominate real-world recommender systems given their ability to learn high-quality, sparse ID-embedding tables that effectively capture user preferences. These tables scale linearly with the number of users and items, and are trained to ensure high similarity between embeddings of interacted user-item pairs, while maintaining low similarity for non-interacted pairs. Despite their high performance, encouraging dispersion for non-interacted pairs necessitates expensive regularization (e.g., negative sampling), hurting runtime and scalability. Existing research tends to address these challenges by simplifying the learning process, either by reducing model complexity or sampling data, trading performance for runtime. In this work, we move beyond model-level modifications and study the properties of the embedding tables under different learning strategies. Through theoretical analysis, we find that the singular values of the embedding tables are intrinsically linked to different CF loss functions. These findings are empirically validated on real-world datasets, demonstrating the practical benefits of higher stable rank -- a continuous version of matrix rank which encodes the distribution of singular values. Based on these insights, we propose an efficient warm-start strategy that regularizes the stable rank of the user and item embeddings. We show that stable rank regularization during early training phases can promote higher-quality embeddings, resulting in training speed improvements of up to 65.9%. Additionally, stable rank regularization can act as a proxy for negative sampling, allowing for performance gains of up to 21.2% over loss functions with small negative sampling ratios. Overall, our analysis unifies current CF methods under a new perspective -- their optimization of stable rank -- motivating a flexible regularization method that is easy to implement, yet effective at enhancing CF systems.
Large Memory Network for Recommendation
Authors: Hui Lu, Zheng Chai, Yuchao Zheng, Zhe Chen, Deping Xie, Peng Xu, Xun Zhou, Di Wu
Venue: WWW
Year: 2025

Modeling user behavior sequences in recommender systems is essential for understanding user preferences over time, enabling personalized and accurate recommendations for improving user retention and enhancing business values. Despite its significance, there are two challenges for current sequential modeling approaches. From the spatial dimension, it is difficult to mutually perceive similar users' interests for a generalized intention understanding; from the temporal dimension, current methods are generally prone to forgetting long-term interests due to the fixed-length input sequence. In this paper, we present Large Memory Network (LMN), providing a novel idea by compressing and storing user history behavior information in a large-scale memory block. With the elaborated online deployment strategy, the memory block can be easily scaled up to million-scale in the industry. Extensive offline comparison experiments, memory scaling up experiments, and online A/B test on Douyin E-Commerce Search (ECS) are performed, validating the superior performance of LMN. Currently, LMN has been fully deployed in Douyin ECS, serving millions of users each day.
LLM-UM: The 1st Workshop on Large Language Model Using Multi-modal Data for User Modeling
Authors: Zhicheng Lu, Mohammad Ali Moni, Yuk Ying Chung, Tom Weidong Cai, Xiaoming Chen
Venue: WWW
Year: 2025

Web-based user modeling is important because it enables personalized experiences by analyzing users' online behavior, preferences, and interactions, allowing systems to deliver tailored content, recommendations, and services in real-time. The workshop will explore cutting-edge approaches to enhancing user models through the integration of diverse data modalities. The discussions will begin with an introduction to the fundamental concepts of large language models (LLMs), including their architectures and applications in user-centric systems. The workshop will also introduce important ethical and technical considerations surrounding multi-modal data use. Issues such as data bias, privacy concerns, and the ethical implications of modeling users based on extensive personal data will be thoroughly examined. Additionally, the workshop will focus on future trends and innovations in the field, including emerging modalities like AR/VR data and the growing importance of real-time adaptation in user models. A discussion will provide an opportunity for collaboration, allowing participants to share their own experiences, challenges, and ideas for pushing the boundaries of multi-modal LLMs in user modeling.
Fast Estimation and Optimization of Resistance Diameter on Graphs
Authors: Zenan Lu, Xiaotian Zhou, Zhongzhi Zhang
Venue: WWW
Year: 2025

The resistance diameter of a graph is the maximum resistance distance among all pairs of nodes in the graph, which has found various applications in many scenarios. However, direct computation of resistance diameter involves the pseudoinverse of graph Laplacian, which takes cubic time and is thus infeasible for huge networks with millions of nodes. In this paper, we consider the computation and optimization problems for resistance diameter of a graph. First, we develop a nearly linear time algorithm to approximate the resistance diameter, which has a theoretically guaranteed error. Then, we propose and study an optimization problem of adding a fixed number of edges to a graph, such that the resistance diameter of the resulting graph is minimized. We show that the objective function is non-supermodular but monotone. Moreover, we propose two fast heuristic algorithms to approximately solve this problem. Finally, we conduct extensive experiments on different networks with sizes up to one million nodes, demonstrating the superiority of our algorithms in terms of efficiency and effectiveness.
Personalizing Data Delivery: Investigating User Characteristics and Enhancing LLM Predictions
Authors: Reuben Luera, Ryan A. Rossi, Franck Dernoncourt, Alexa F. Siu, Sungchul Kim, Tong Yu, Ruiyi Zhang, Xiang Chen, Nedim Lipka, Zhehao Zhang, Seon Gyeom Kim, Tak Yeon Lee
Venue: WWW
Year: 2025

Presenting data in a format that closely aligns with user preferences-whether as a chart table or text output-drastically enhances the user experience of data-focused systems. Exploring these preferences enables us to understand when it is best to show a chart, table, or text to the user for a given question. For this, we conduct a user study where users are shown a question and asked what data type they would prefer to see. Understanding how user characteristics impact a user's preferences is critical to creating data tools with a better user experience. Additionally, we investigate to what degree an LLM can be used to replicate a user's preference with and without user preference data. Overall, these findings have significant implications pertaining to the development of data tools and the replication of human preferences using LLMs. An LLM's ability to effectively replicate human preferences can pave the way for synthetic data to supplement human input. All in all, these findings highlight the significance of data tool personalization, while also laying the foundation for further applications of LLMs.
NFTs as a Data-Rich Test Bed: Conspicuous Consumption and its Determinants
Authors: Taylor Lundy, Narun K. Raman, Scott Duke Kominers, Kevin Leyton-Brown
Venue: WWW
Year: 2025

Conspicuous consumption occurs when a consumer derives value from a good based on its social meaning as a signal of wealth, taste, and/or community affiliation. Common conspicuous goods include designer footwear, country club memberships, and artwork; conspicuous goods also exist in the digital sphere, with non-fungible tokens (NFTs) as a prominent example. The NFT market merits deeper study for two key reasons: first, it is poorly understood relative to its economic scale; and second, it is unusually amenable to analysis because NFT transactions are publicly available on the blockchain, making them useful as a test bed for conspicuous consumption dynamics. This paper introduces a model that incorporates two previously identified elements of conspicuous consumption: the bandwagon effect (goods increase in value as they become more popular) and the snob effect (goods increase in value as they become rarer). Our model resolves the apparent tension between these two effects, exhibiting net complementarity between others' and one's own conspicuous consumption. We also introduce a novel dataset combining NFT transactions with embeddings of the corresponding NFT images computed using an off-the-shelf vision transformer architecture. We use our dataset to validate the model, showing that the bandwagon effect raises an NFT collection's value as more consumers join, while the snob effect drives consumers to seek rarer NFTs within a given collection.
Integrating Large Language Models and Knowledge Graphs for Next-level AGI
Authors: Linhao Luo, Carl Yang, Evgeny Kharlamov, Shirui Pan
Venue: WWW
Year: 2025

Large language models (LLMs), due to their emergent ability and generalizability, are making new waves in developing Artificial General Intelligence (AGI). However, LLMs are black-box models, which often fall short of capturing and accessing factual knowledge. In contrast, Knowledge Graphs (KGs) are structured knowledge models that explicitly store rich factual knowledge. KGs can enhance LLMs by providing external knowledge for inference and interpretability. Meanwhile, KGs are difficult to construct and evolve by nature, which challenges the existing methods in KGs to generate new facts and represent unseen knowledge. Therefore, it is complementary to integrating LLMs and KGs together and simultaneously leveraging their advantages to achieve AGI's ultimate goal: to reason, adapt, and synthesize knowledge with human-level nuance and factual accuracy. This tutorial aims to bridge this gap by presenting a comprehensive overview of the unification of LLMs and KGs for next-level AGI. Specifically, we will cover three key frameworks: (1) KG-enhanced LLMs, which focus on augmenting LLMs with KGs for pre-training, fine-tuning, and inference, thereby enriching the LLMs' factual and contextual accuracy; (2) LLM-augmented KGs, which leverage LLMs to assist in tasks such as KG completion, construction, and question answering, ultimately facilitating KG scalability and adaptability; and (3) Synergized LLM-KG Systems and Applications, where LLMs and KGs function symbiotically to enable real-time, bidirectional reasoning, transforming static knowledge structures into dynamic, AGI-driven frameworks. Through this tutorial, participants will gain a structured understanding of the architectures, underlying methodologies, and key advancements in LLM-KG unification, alongside insights into current real-world applications and challenges. We will also explore future research directions, encouraging the development of innovative AGI systems that are not only knowledgeable but also faithful in reasoning. This tutorial will empower researchers and practitioners to unlock the next level of AGI by integrating the strengths of LLMs and KGs into cohesive, intelligent systems.
EvolutionAgent: A Large Model-Based Framework for User Behavior Modeling and Self-Evolving Intelligent Agents
Authors: Junhui Luo, Li Song, Ranrun Sun
Venue: WWW
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable capabilities in simulating user behavior, offering significant potential for user modeling, behavior analysis, interest matching, and the extraction of unstructured features. However, the process of user simulation necessitates the identification of both unstructured and structured features, as well as the formulation of multi-stages workflow, which remains a challenging and labor-intensive task. To address this, we propose EvolutionAgent , a novel framework grounded in a discretized material repository, which employs self-reflective and evolutionary mechanisms to automate the search for unstructured features and the generation of simulation workflow. This framework establishes an efficient and robust simulation mechanism, achieving state-of-the-art performance across three distinct datasets. Notably, EvolutionAgent exhibits exceptional robustness, even in scenarios with limited historical data for users and products, underscoring its adaptability and reliability.
Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
Authors: Jiayi Luo, Qingyun Sun, Haonan Yuan, Xingcheng Fu, Jianxin Li
Venue: WWW
Year: 2025

Adversarial evasion attacks pose significant threats to graph learning, with lines of studies that have improved the robustness of Graph Neural Networks (GNNs). However, existing works rely on priors about clean graphs or attacking strategies, which are often heuristic and inconsistent. To achieve robust graph learning over different types of evasion attacks and diverse datasets, we investigate this problem from a prior-free structure purification perspective. Specifically, we propose a novel Diffusion-based Structure Purification framework named DiffSP, which creatively incorporates the graph diffusion model to learn intrinsic distributions of clean graphs and purify the perturbed structures by removing adversaries under the direction of the captured predictive patterns without relying on priors. DiffSP is divided into the forward diffusion process and the reverse denoising process, during which structure purification is achieved. To avoid valuable information loss during the forward process, we propose an LID-driven non-isotropic diffusion mechanism to selectively inject noise anisotropically. To promote semantic alignment between the clean graph and the purified graph generated during the reverse process, we reduce the generation uncertainty by the proposed graph transfer entropy guided denoising mechanism. Extensive experiments demonstrate the superior robustness of DiffSP against evasion attacks.
Omni-SILA: Towards Omni-scene Driven Visual Sentiment Identifying, Locating and Attributing in Videos
Authors: Jiamin Luo, Jingjing Wang, Junxiao Ma, Yujie Jin, Shoushan Li, Guodong Zhou
Venue: WWW
Year: 2025

Prior studies on Visual Sentiment Understanding (VSU) primarily rely on the explicit scene information (e.g., facial expression) to judge visual sentiments, which largely ignore implicit scene information (e.g., human action, objection relation and visual background), while such information is critical for precisely discovering visual sentiments. Motivated by this, this paper proposes a new Omni-scene driven visual Sentiment Identifying, Locating and Attributing in videos (Omni-SILA) task, aiming to interactively and precisely identify, locate and attribute visual sentiments through both explicit and implicit scene information. Furthermore, this paper believes that this Omni-SILA task faces two key challenges: modeling scene and highlighting implicit scene beyond explicit. To this end, this paper proposes an Implicit-enhanced Causal MoE (ICM) approach for addressing the Omni-SILA task. Specifically, a Scene-Balanced MoE (SBM) and an Implicit-Enhanced Causal (IEC) blocks are tailored to model scene information and highlight the implicit scene information beyond explicit, respectively. Extensive experimental results on our constructed explicit and implicit Omni-SILA datasets demonstrate the great advantage of the proposed ICM approach over advanced Video-LLMs.
ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoning
Authors: Pengfei Luo, Jingbo Zhou, Tong Xu, Yuan Xia, Linli Xu, Enhong Chen
Venue: WWW
Year: 2025

With the proliferation of images in online content, language-guided image retrieval (LGIR) has emerged as a research hotspot over the past decade, encompassing a variety of subtasks with diverse input forms. While the development of large multimodal models (LMMs) has significantly facilitated these tasks, existing approaches often address them in isolation, requiring the construction of separate systems for each task. This not only increases system complexity and maintenance costs, but also exacerbates challenges stemming from language ambiguity and complex image content, making it difficult for retrieval systems to provide accurate and reliable results. To this end, we propose ImageScope, a training-free, three-stage framework that leverages collective reasoning to unify LGIR tasks. The key insight behind the unification lies in the compositional nature of language, which transforms diverse LGIR tasks into a generalized text-to-image retrieval process, along with the reasoning of LMMs serving as a universal verification to refine the results. To be specific, in the first stage, we improve the robustness of the framework by synthesizing search intents across varying levels of semantic granularity using chain-of-thought (CoT) reasoning. In the second and third stages, we then reflect on retrieval results by verifying predicate propositions locally, and performing pairwise evaluations globally. Experiments conducted on six LGIR datasets demonstrate that ImageScope outperforms competitive baselines. Comprehensive evaluations and ablation studies further confirm the effectiveness of our design.
MerKury: Adaptive Resource Allocation to Enhance the Kubernetes Performance for Large-Scale Clusters
Authors: Jiayin Luo, Xinkui Zhao, Yuxin Ma, Shengye Pang, Jianwei Yin
Venue: WWW
Year: 2025

As a dominant paradigm in modern web applications, cloud computing has seen a surge in adoption. The deployment of vast and various workloads encapsulated within containers has become ubiquitous across cloud platforms, imposing substantial demands on the supporting infrastructure. However, Kubernetes (k8s), the de facto standard for container orchestration, struggles with low scheduling throughput and high latency in large-scale clusters. The primary challenges are identified as excessive load from read requests and resource contention between co-located components. In this paper, we present MerKury, a general and lightweight framework designed to enhance the Kubernetes performance for large-scale clusters. MerKury employs a dual strategy: first, it preprocesses specific requests to alleviate excessive load; second, it introduces an adaptive resource allocation algorithm to mitigate resource contention. Evaluations across various cluster scales demonstrate that MerKury notably augments node capacity by up to 4.5×, increases scheduling throughput by up to 7.3×, and reduces request latency by 5.6%-57.7%, outperforming vanilla Kubernetes and baseline resource allocation methods.
Grasp the Key Takeaways from Source Domain for Few Shot Graph Domain Adaptation
Authors: Xiangwei Lv, Jingyuan Chen, Mengze Li, Yongduo Sui, Zemin Liu, Beishui Liao
Venue: WWW
Year: 2025

Graph Neural Networks (GNNs) have achieved remarkable success in node classification tasks on individual graphs. However, existing GNNs trained within a specific domain ( a.k.a., source domain) frequently exhibit unsatisfied performance when transferred to another domain ( a.k.a., target domain), due to the domain gap. To tackle this issue, Few Shot Graph Domain Adaptation (FSGDA) is introduced to the node classification task, facilitating knowledge transfer from a fully labeled source graph to a target graph with minimal annotations for each class. An intuitive solution is directly training the GNN with labeled source and target samples together. Nevertheless, there are two issues in this procedure: (1) When the annotations on the target domain used for training are extremely sparse, the GNN performance may significantly be damaged by nodes with the source-domain bias not aligning with the target-domain distribution. (2) Apart from the biased nodes, the low-value nodes among the remaining nodes impede the GNN learning for the core nodes, like the limited target training nodes. To address the above issues, we propose a new method for FSGDA, named GraphInflu, whose core idea is to grasp the key takeaways from the source domain to facilitate the adaptation process. It contains two characteristic modules, including the Supportive Node Selector and the Soft Logic-Inspired Node Reweighting. The former aims to identify the most influential set of source nodes based on their contribution to improving performance on target nodes. The latter further focuses more on the core nodes in the selected influential set, which closely align with the target nodes especially those presenting challenging predictions. Extensive experiments validate the efficacy of GraphInflu by overcoming the current state-of-the-art methods. Our code is available at https://github.com/lvXiangwei/GraphInflu.git.
Dynamic Topic Segmentation in Dialogues: Enhancing Boundaries with Topic-Aware Propagation
Authors: Yantong Lv, Wanjie Tao, Qun Dai, Zulong Chen, Quan Lu, Ning Jiang
Venue: WWW
Year: 2025

Dialogue Topic Segmentation (DTS) forms the foundation for various dialogue modeling tasks. However, existing methods do not fully leverage crucial dialogue information, such as positional cues and inter-topic associations. Additionally, these segmentation strategies lack flexibility, as they typically rely on a one-time global semantic comparison to determine segmentation boundaries without accounting for the dynamic influence of topic shifts on semantics. To address these limitations, we propose an innovative dynamic Dialogue Topic Segmentation (DyDTS) framework that employs a dynamic boundary optimization strategy based on topic-aware propagation matching, which enhances boundary refinement. Experimental results on two public datasets show the proposed method outperforms mainstream baselines. The source code and datasets used in this study are available at:https://github.com/Mark131434/DyDTS.